An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell

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An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell Partha Pratim Banik , Rappy Saha , Ki-Doo Kim PII: DOI: Reference:

S0957-4174(20)30037-3 https://doi.org/10.1016/j.eswa.2020.113211 ESWA 113211

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Expert Systems With Applications

Received date: Revised date: Accepted date:

16 August 2019 3 January 2020 15 January 2020

Please cite this article as: Partha Pratim Banik , Rappy Saha , Ki-Doo Kim , An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell, Expert Systems With Applications (2020), doi: https://doi.org/10.1016/j.eswa.2020.113211

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Highlights

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A generalized white blood cell (WBC) nucleus segmentation method is proposed. We localize WBC by showing a statistical analysis on four public databases. We compare the proposed WBC nucleus segmentation method on four public databases. A new CNN model is proposed to classify four types of white blood cell. We evaluate and compare the proposed CNN model by using nine evaluation metrics.

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An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell Partha Pratim Banik a, Rappy Saha a, and Ki-Doo Kim a,* a

School of Electronics Engineering, Kookmin University, Seoul 02707, Korea

Abstract White blood cells (WBCs) play a remarkable role in the human immune system. To diagnose bloodrelated diseases, pathologists need to consider the characteristics of WBC. The characteristics of WBC can be defined based on the morphological properties of WBC nucleus. Therefore, nucleus segmentation plays a vital role to classify the WBC image and it is an important part of the medical diagnosis system. In this study, color space conversion and k-means algorithm based new WBC nucleus segmentation method is proposed. Then we localize the WBC based on the location of segmented nucleus to separate them from the entire blood smear image. To classify the localized WBC image, we propose a new convolutional neural network (CNN) model by combining the concept of fusing the features of first and last convolutional layers, and propagating the input image to the convolutional layer. We also use a dropout layer for preventing the model from overfitting problem. We show the effectiveness of our proposed nucleus segmentation method by evaluating with seven quality metrics and comparing with other methods on four public databases. We achieve average accuracy of 98.61% and more than 97% on each public database. We also evaluate our proposed CNN model by using nine classification metrics and achieve an overall accuracy of 96% on BCCD test database. To validate the generalization capability of our proposed CNN model, we show the training and testing accuracy and loss curves for random test set of BCCD database. Further, we compare the performance of our proposed CNN model with four state-of-the-art CNN models (biomedical image classifier) by measuring the value of evaluation metrics. *

Corresponding author E-mail address: [email protected] (Ki-Doo Kim), Tel: +82-2-910-4707, Fax: +82-2-910-4449, [email protected] (Partha Pratim Banik), [email protected] (Rappy Saha).

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Keywords: white blood cell, nucleus segmentation, CNN, convolutional layer, classification metrics

1. Introduction Blood cells are one of the essential cells in the human body and include red blood cells, white blood cells (WBCs), and platelets. WBCs are the cells of the immune system; therefore, they are also called immune cells. They protect the human body from various types of infectious diseases and extrinsic invaders. Majority of the WBCs originate from red bone marrow, whereas some originate from important glands in the body. Although WBCs are colorless, hematologists can use special stains to view them under the microscope (Medical Technology, University of Wisconsin Oshkosh, 2018). They are classified as either granular or non-granular cells. The granular cells are Neutrophils, Eosinophils, and Basophils. The non-granular (single-nucleus) cells are Monocytes and Lymphocytes (Medical Technology, University of Wisconsin Oshkosh, 2018). Macawile et al. (2018) describe that various severe diseases (e.g., bacterial infections, acquired immune deficiency syndrome, and cancer) can occur because of an abnormal range of four types of WBCs (Neutrophils, Eosinophils, Monocytes, and Lymphocytes), which can be used to infer that these four types are important for diagnosing blood-related diseases (Macawile et al., 2018). In addition, the number of available Basophil images is very few because of their low occurrence (0%–3%) in peripheral blood (Sabino, Costa, Rizzatti, & Zago, 2004). Therefore, because of their low importance in disease diagnosis and the unavailability of data for the Basophil-type WBC, we consider only four types of WBCs in our research work. The typical number of WBCs in a healthy human being ranges from four to eleven thousand per cubic inch of blood (Medical Technology, University of Wisconsin Oshkosh, 2018). WBC counting work generally includes WBC image segmentation and classification. The expert hematologists have to manually segment the WBC and classify the type of WBC, which is very tedious and timeconsuming job, and the accuracy of them directly affects the medical disease diagnosis and doctors‟ treatment decision. So, medical blood disease diagnosis and corresponding treatment largely

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depends on WBC segmentation and classification process. However, WBC segmentation and classification is a challenging job because of their irregular shape and sizes. Therefore, a study on WBC nucleus segmentation and classification has significant value in the field of medical diagnosis systems. Industrial automated cell morphology (ACM) system like Hema-CAM (Vision Hema, 2019), Cella-Vision (http://www.cellavision.com), MEDICA EasyCell Assistant (Sysmex, 2019) are used to classify the white blood cell (WBC). These ACM devices classify the WBC by the following steps, as shown in Fig. 1. Pre-processing stage consists of image de-noising, contrast enhancement by histogram matching or average filtering and color correction. This stage is utilized for increasing clear visibility of the input WBC image. The presence of this stage does depend on the quality of the camera which is used for the acquisition of WBC image. Then, in segmentation stage, the WBC is segmented into three different regions: a) nucleus region, b) cytoplasm region, and c) cell region (nucleus and cytoplasm). Experts use every region to extract the feature of the WBC. So, segmentation step is one of the most important steps. After that, in feature extraction stage, the morphological properties (e.g. color, texture, shape, spectral (Wang et al., 2016), wavelet (Abedini, Firouzmand, & Razavi, 2013) of nucleus and cytoplasm regions are extracted as well as utilized for the selection purpose. Feature selection step includes well known algorithm like principal component analysis, chi-squared techniques, etc. It decreases the dimension of extracted feature vectors as well as it also decreases any kind of post processing time. Finally, through the multi-step classification or majority-voting classification decides the type of WBC. These are the traditional steps of industrial WBC classification system.

Figure 1. Industrial WBC classification system.

Now we follow up some major disadvantages of these procedures of traditional WBC classification system. We observe that into any specialized or generalized pre-processing steps are

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not available in versatile types of input WBC image. Because, blood smear images can be affected by different conditions, light distribution and variation of staining intensities. Any kind of segmentation algorithms can be influenced by this kind of problem that can reduce the rate of segmentation. This is also a major challenge to generalize any segmentation algorithm. Feature selection step may reduce the classification accuracy (Rezatofighi & Soltanian-Zadeh, 2011). Multistage classification (Yu et al., 2017) not only increases the system complexity but also consumes a lot of processing time. For all of the above reasons, traditional method cannot achieve higher accuracy in WBCs classification. These disadvantages of traditional procedures of WBC classification system motivate us to design a nucleus segmentation and CNN model based classification system where problem of pre-processing, and dataset dependency of segmentation methods have been overcome through the evaluation and comparison of our proposed methods with other methods on several public databases. In this paper, we propose WBC nucleus segmentation method, based on the HSI and L*a*b color space, and k-means algorithm (Arthur & Vassilvitskii, 2007). Then we propose convolutional neural network (CNN) model based classification method. For this purpose, we use Blood Cell Count and Detection (BCCD) database (i.e., a small-scale dataset for blood cell detection) (Mooney, 2019, and Cheng, 2018) to develop and test our WBC classification method. We have organized our entire work in six sections: In section II, we describe some related works on WBC nucleus segmentation and WBC classification. Section III describes the proposed method. Section IV describes the databases used for WBC nucleus segmentation and WBC classification. Section V not only delineates the performance of the proposed method but also makes a comparison with other state-of-the-art WBC nucleus segmentation and WBC classification methods. Section VI concludes the manuscript.

2. Related Works and Our Contributions In this section, we describe the segmentation methods which are designed based on color space

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information, Otsu threshold, k-means algorithm, region growing algorithm, and k-nearest neighbor (KNN) algorithm. We also describe machine learning based classification approaches. We analyze the advantages and disadvantages of these methods, which motivate us to develop a generalized WBC nucleus segmentation and classification method. 2.1. Methods of WBC Nucleus Segmentation In this section, we describe the overview of several segmentation techniques and limitations of them through Table-1: Table-1: Overview of several segmentation methods.

Nucleus segmentation methods

Overview of segmentation techniques

Nasir, Mashor, & Rosline, 2011

Applying linear contrast technique, conversion to HSI color space, kmeans on H component (k=3), median filtering, region growing, k-means on S component (k=3), median filtering to segment nucleus.

Mohapatra, Samanta, Patra, & Satpathi, 2011

Median filtering, conversion to L*a*b color space, applying GKmeans algorithm (k=4), nearest neighbor classification, blue color base nucleus segmentation.

Mohamed, Far, & Guaily, 2012

Conversion to greyscale, applying linear contrast stretching (L) and histogram equalization (H), arithmetic operation on L and H, median filtering 3 times, applying Otsu method for measuring global threshold to segment nucleus.

Arslan, Ozyurek, & Gunduz-Demir, 2014

Subtraction B (blue) and G (green) component of RGB image, obtaining a threshold by using Otsu‟s threshold, applying watershed algorithm to segment the nucleus.

Nazlibilek et al., 2014

Conversion to greyscale, applying Otsu‟s method to obtain binary image, morphological operation to segment the nucleus.

Limitations - Use preprocessing stage. - Segmentation method is designed for lymphocyte WBC. - Dependent on H and S component of HSI color space. - WBC detection depends on number of pixels. - Number of test images: 100 - Use preprocessing stage. - Method proposed for lymphocyte WBC. - Color dependent nucleus segmentation. - Number of test images: 108

- Dependent on Otsu‟s threshold. - Number of test images: 365

- Dependent on B and G component of RGB image. - Dependent on Otsu‟s threshold. - Specific parameter dependent (i.e. depth threshold, circle radius, radius width). - Number of test images: 650 - Dependent on Otsu‟s threshold. - Morphological operation is carried out by measuring the axis length and axis length threshold is used to segment nucleus.

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- Number of test images: 240 Prinyakput & Pluempitiwiriyawej, 2015

Amin, Kermani, Talebi, & Oghli, 2015

Sarrafzadeh, Dehnavi, Rabbani, & Talebi, 2015

Vincent, Kwon, Lee, & Moon, 2015

Vogado et al., 2016

Kumar & Vasuki, 2017

Abdeldaim, Sahlol, Elhoseny, & Hassanien, 2018

The sum of R (red) and B (blue) channels and divide the summation by G channels, applying Otsu‟s method for image binarisation, morphological operation to segment nucleus. Conversion to HSI color space, applying k-means (k=4), the mean value of R (red) channel for each cluster, cluster selection based on the lowest mean value of R channel to segment nucleus. Applying median and Gaussian filter to remove noise, conversion to L*a*b space, applying k-means (k=3) on „a‟ and „b‟ component, nucleus segmentation by taking the cluster with highest value of „a‟ component and lowest value of „b‟ component. Conversion to L*a*b color space, applying k-means (k=3), contrast enhancement, applying Otsu‟s method, morphological operations to segment nucleus. Conversion to CMYK and L*a*b color space, contrast adjustment on M and „b‟ component by median filtering, component „b‟ subtracted from M, applying k-means (k=3) to segment nucleus. Conversion to HSI, applying kmeans (k=4) on H and S component, cluster selection with the lowest R (red) value to segment nucleus. Conversion to CMYK color space, applying triangular algorithm (Zack, Rogers, & Latt, 1977), histogram analysis based nucleus segmentation.

- RGB channel dependent nucleus segmentation. - Dependent on Otsu‟s threshold.

- Dependent on R (red) channel. - Dependent on Otsu‟s threshold. - Number of test images: 312

- Use preprocessing stage. - Dependent on „a‟ and „b‟ component of L*a*b color space. -Number of test images: 195

- Method designed for lymphocyte WBC - Parameter of morphological operation depends on image database. - Number of test images: 100 - Dependent on M (of CMYK) and b (of L*a*b) component. - Contrast adjustment is necessary, otherwise nucleus cannot be segmented. - Number of test images: 735 - Dependent on R (red) channel. - Number of test images: 70 - Segmentation method is designed for lymphocyte WBC. - Their segmentation algorithm is ineffective while the object pixels produce strong peak. - Number of images: 260.

The methods of (Nasir, Mashor, & Rosline, 2011; Mohapatra, Samanta, Patra, & Satpathi, 2011; Vincent, Kwon, Lee, & Moon, 2015; and Abdeldaim, Sahlol, Elhoseny, & Hassanien, 2018) are not designed for all kinds of WBC. They specifically design their methods on lymphocyte WBC. Our proposed nucleus segmentation method is designed not only lymphocyte but also eosinophil,

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neutrophil, and monocyte WBCs. Preprocessing stage is one kind of bottleneck step which is designed based on the condition of image database. So, preprocessing based methods (methods of (Nasir, Mashor, & Rosline, 2011; Mohapatra, Samanta, Patra, & Satpathi, 2011; Sarrafzadeh, Dehnavi, Rabbani, & Talebi, 2015; and Vogado et al., 2016)) cannot be used as the generalized nucleus segmentation method. In this study, we do not apply any preprocessing step to design our nucleus segmentation method. Specific component (e.g. red or blue or hue and saturation or „a‟ and „b‟ or „M‟) of color space based nucleus segmentation methods (methods of (Nasir, Mashor, & Rosline, 2011; Mohapatra, Samanta, Patra, & Satpathi, 2011; Arslan, Ozyurek, & Gunduz-Demir, 2014; Prinyakput & Pluempitiwiriyawej, 2015; Amin, Kermani, Talebi, & Oghli, 2015; and Kumar & Vasuki, 2017)) and threshold based methods (methods of (Nasir, Mashor, & Rosline, 2011; Mohapatra, Samanta, Patra, & Satpathi, 2011; Arslan, Ozyurek, & Gunduz-Demir, 2014; Nazlibilek et al., 2014; Prinyakput & Pluempitiwiriyawej, 2015; and Amin, Kermani, Talebi, & Oghli, 2015)) limit the applicability on different databases. Our proposed segmentation method is independent of this kind of limitation. The method of (Vincent, Kwon, Lee, & Moon, 2015) has specific parameter dependency which also limits the applicability of their method. All of the segmentation methods except the segmentation method of (Prinyakput & Pluempitiwiriyawej, 2015), listed in Table-1, are tested on 70 to 735 number of images. Our proposed segmentation method has no parameter dependency and number of test images are larger than all these segmentation methods. 2.2. Machine-Learning-based WBC Classification Techniques Machine learning is a popular technique in the fields of digital medical imaging, medical image analysis, and computer-aided diagnosis systems. For medical pattern recognition, learning from samples is a vital requirement. In 1972, Young segmented WBCs using the concept of histogram thresholds and classified cells using a distance classifier. They train their algorithm on 74 cells with limited number of features (=4). He tested on 199 WBC images, and the classification accuracy was 92.46% (Young, 1972). Training on limited number of hand crafted features are the only limitation of their method. Prinyakupt & Pluempitiwiriyawej proposed a segmentation-based linear and

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Bayesian classifier and their linear classifier achieved maximum accuracy (98%) than Bayesian classifier (Prinyakupt & Pluempitiwiriyawej, 2015). They estimated 15 features (hand crafted) and their classification method follows the color channel (red and blue) based decision. They tested their method on 1078 images. Their method depends on the setting of parameters based on the test image resolution and sizes. Hand crafted features, color channel based decision making, and database dependent parameter are the limitations of their classification method. Rezatofighi & SoltanianZadeh showed the comparison between support vector machine (SVM) and multi-layer perceptron (MLP) based classification technique to classify WBCs. They estimate morphological, and texture features (in total 47 features) and because of estimating 14 texture features by co-occurrence matrix, the feature extraction step consumes longer processing time. They claimed that hematologist can use their method in laboratory because of their reasonable processing time (10s per image). For testing their classification method, they use 100 WBC images and their classification accuracy is 96% (Rezatofighi & Soltanian-Zadeh, 2011). The main limitations of their method are the processing speed and dependency of handcrafted features. Su, Cheng, & Wang measure geometrical, color, and local-directional-pattern (LDP) features (20 features) and feed them to MLP, SVM, and hyperrectangular composite neural networks (HRCNNs) to classify the WBC (Su, Cheng, & Wang, 2014). The LDP features are approximated on 60 cell images which means empirical analysis. So, without empirical analysis on any database, hematologists cannot estimate the LDP features to classify WBCs. It is a great disadvantage of their classification method. They tested on 151 images. Nazlibilek et al. use two MLP networks to classify the WBC. First MLP takes segmented WBC as input and another MLP takes 242 inputs by applying principle component analysis (PCA) on the dataset (Nazlibilek et al., 2014). They show that 2nd MLP (65% accuracy) works well than 1st MLP (95% accuracy). Optimum number of features drawing from segmented WBC image by applying PCA is one of the contributions of them. But tested on few number of images (60) is a limitation of their method that indicates the lack of validation.

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From the discussion on above classification methods, we come to know that hand crafted feature estimation, color channel based decision, database dependent parameter, high processing time consumption, and database dependent feature (LDP feature) estimation are the existing limitations of their proposed classification methods. In our proposed classification method, the use of CNN model removes the limitation of hand crafted and database dependent feature extraction because it extracts the features automatically during the model training. Moreover, our classification method also overcomes the limitations of database dependent parameter selection, and high processing time consumption. Convolutional neural networks are very well known in the field of pattern recognition. Nowadays, CNN has been broadly implemented in various image classification fields. Liang, Hong, Xie, & Zheng described a novel model that combined a CNN and a recurrent neural network (RNN) which is called CNN–RNN model (Liang, Hong, Xie, & Zheng, 2018). They suggested that an RNN with a memory function generates a continuous state of time output for features that are extracted from the CNN. Based on this idea, their model achieved a remarkable recognition rate, about 90.79%, on BCCD test database. In (Novoselnik, Grbić, Galić, & Dorić, 2018), authors used a modified LeNet5 CNN model (Lecun, Bottou, Bengio, & Haffner, 1998) to classify them. They omitted basophils, which represented only 0.01% of the dataset. They evaluate their model performance on 85 imagesand achieve 81.1% accuracy. Yu et al. use a multi CNN model based majority voting classification system. They modify ResNet50 (He, Zhang, Ren, & Sun, 2016), inception V3 (Szegedy et al., 2015b), VGG16, VGG19 (Simonyan & Zisserman, 2014), and Xception (Chollet, 2016) model into the fully connected layer. But they do not mention any training parameter, specific number of neurons, and also do not show any learning curve. They use 1000 images to test their classification system and achieve 88.5% accuracy. Their classification system consumes higher processing time. This is the main disadvantage of their classification system. Banik, Saha, & Kim show a CNN model that fuses the shallow and deep features. Their CNN model is based on the concept of (Pang, Du, Orgun, & Yu, 2019). They train and test their CNN model on BCCD database

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and achieve 90.39%. Tiwari et al. propose a three layer based CNN model which is very small and naïve model (Tiwari et al., 2018). Because of their naïve model, they achieve lower accuracy (61%) on Neutrophil and Eosinophil WBC. Pang, Du, Orgun, & Yu propose a novel CNN model that can fuse the shallow and deep features. Their CNN model is for the application of inter-class classification CT and MRI images. The analysis of previous related works proves that distance, SVM and Bayesian classifier (Young, 1972; Prinyakupt & Pluempitiwiriyawej, 2015; Rezatofighi & Soltanian-Zadeh, 2011), none of them evaluated their classifiers on any public database. Most of them achieved remarkable accuracy (93% to 96%) on small-scale private databases. Only (Liang, Hong, Xie, & Zheng, 2018) used BCCD database but they cannot achieve remarkable accuracy like (Young, 1972; Prinyakupt & Pluempitiwiriyawej, 2015; and Rezatofighi & Soltanian-Zadeh, 2011). In this paper, we design a new CNN model that can overcome this limitation. 2.3. Our Contributions The contributions of this paper are summarized as follows: 1. As our proposed WBC nucleus segmentation method does not depend on any specific color channel, it is risk-free under various imaging and staining condition of blood smear image. 2. On four public databases, we show the better performance of our proposed WBC nucleus segmentation method than other nucleus segmentation methods (Nasir, Mashor & Rosline, 2011; Nazlibilek et al., 2014; Vogado et al., 2016; Kumar & Vasuki, 2017) that proves our method as the generalized and database-independent method. 3. We show the WBC nucleus location based statistical analysis on four public databases to localize the WBC which is independent of WBC cytoplasm color and property. 4. In this paper, we propose a new CNN model by utilizing the information of input image to the convolutional layer through overlapping average pooling operation and fusing the features of first and last convolutional layer. This property improves the performance of

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our proposed CNN model than other CNN models (Yu et al., 2017; Liang, Hong, Xie, & Zheng, 2018; Pang, Du, Orgun, & Yu, 2019; and Banik, Saha, & Kim, 2019).

3. Proposed Method We divide this section into three sub-sections: WBC nucleus segmentation method, WBC localization method, and CNN model based classification method. Figure 2 shows the whole process of the proposed method. As shown in Fig. 2, at first, we segment the nucleus of WBC by applying color space conversion and k-means algorithm. After that, we localize the WBC based on the location of nucleus and crop the WBC from the entire blood smear image by using the location of WBC. Finally, a new CNN model, based on the concept of fusing first and last convolutional features and propagating input image to each layer, is used to classify the type of cropped WBC.

Figure 2. The whole process of the proposed methods.

3.1. Proposed WBC Nucleus Segmentation Method WBC nucleus segmentation algorithm is described in Algorithm 1. To develop the WBC nucleus segmentation method, at step 1, we transform RGB image to HSI and L*a*b color spaces. We measure the mean intensity on each component (hue, saturation and intensity) of the HSI and L*a*b color spaces. Then we take the corresponding component having minimum mean value among the three mean values of HSI, shown in step 2 and we call it as comp1. In the same manner, we also measure comp2 of L*a*b, shown in step 3. They (comp1 and comp2) are in two dimensional matrix format, H×W. Here, H and W are the height and width of input image, respectively. Figure 3(b) and 3(c) show the result of comp1 and comp2 of a sample WBC image, respectively. In step 4, we measure mul_comp_i which represents the element wise multiplication of comp1 and comp2 image

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matrix and divide them with the average of comp1. The symbol “◦” shown in step 4, indicates the element wise multiplication. Figure 3(d) shows the output image of mul_comp_i. After this step, we can obtain separated nucleus for a WBC because the nucleus of Neutrophil and Eosinophil is separated. For this reason, we blur the mul_comp_i image in step 5 and we call it, blur_i. Because of this step, the separated nucleus of a WBC becomes connected, as shown in Fig. 3(e). We apply kmeans clustering algorithm on blur_i image with two clusters in step 6. Figure 3(f) shows the clustered image. The reason of choosing two clusters is that, in mul_comp_i image, it has only two kinds of clearly visible objects (dominant pixels): background and nucleus, as shown in Fig. 3(d). As we set the number of clusters (k) two in k-means algorithm, there is no chance to have more than two clusters. But there are also some objects (red blood cell or granules of WBC cytoplasm or platelets) which are not as clearly visible as nucleus. As k-means clustering algorithm emphasizes on dominant pixels, it will obviously cluster the nucleus with a background. For this reason, we use k-means clustering algorithm in this stage to ensure the segmentation of nucleus. We use the MATLAB built-in function of the k-means algorithm (Arthur & Vassilvitskii, 2007). At the end of step 6, we store the clustered intensity. After that, we take the maximum intensity of clust_clr, nuc_i in step 7. Finally, in step 8, we apply the hole filling operation on nuc_i to segment the WBC nucleus, shown in Fig. 3(g) and we call it, seg_nuc_i.

(a) (b) (c) (d) (e) (f) (g) Figure 3. The output images of Algorithm 1. (a) Input RGB image (Neutrophil), (b) comp1, (c) comp2, (d) mul_comp_i, (e) blur_i, and (f-g) seg_nuc_i (segmented nucleus).

In our nucleus segmentation method (Algorithm-1), we do not have any specific color channel dependency to highlight or segment WBC nucleus which is present in (Nasir, Mashor, & Rosline, 2011; Mohapatra, Samanta, Patra, & Satpathi, 2011; Arslan, Ozyurek, & Gunduz-Demir, 2014; Prinyakput & Pluempitiwiriyawej, 2015; Amin, Kermani, Talebi, & Oghli, 2015; Kumar & Vasuki,

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2017; and Vogado et al., 2016). In Algorithm-1, first four steps are the main important points to highlight the WBC nucleus. We also use k-means algorithm with two clusters which is lower than the segmentation methods discussed in section 2.1. These are the main advantages of our proposed nucleus segmentation method (Algorithm-1) over other segmentation methods, discussed in section 2.1. Algorithm 1: WBC Nucleus Segmentation Algorithm 1. Convert input image from RGB to HSI and L*a*b color space. 2. comp1 = Taking the corresponding component (matrix) which has the minimum average value among the components of HSI. 3. comp2 = Taking the absolute value of corresponding component (matrix) which has the minimum average value among the components of L*a*b. 4. mul_comp_i = (comp1◦comp2)/mean(comp1). 5. blur_i = Applying circular average filter to blur the mul_comp_i. 6. clust_clr = Applying k-means clustering algorithm on blur_i image to cluster it on two clusters. Store the intensity of two clusters. 7. nuc_i = Taking maximum intensity of clust_clr to segment the nucleus. 8. seg_nuc_i = Applying hole filling operation on nuc_i image to fill up the holes of nuc_i.

3.2. Proposed WBC Localization Method In this section, we localize the WBC based on the location of nucleus. Algorithm 2 shows the steps of the localization of WBC. From step 1 to 4, we count the number of total segmented nucleus. After this step, we measure the row and column location of nucleus from step 5 to 11. To localize the WBC, we also need to segment the cytoplasm. But the color, shape and characteristics of cytoplasm are different in different databases, shown in Fig. 4. So, a generalized algorithm is difficult to develop that can segment these diverse characteristics of cytoplasm for all databases. For this reason, we perform a statistical analysis on the ratio of WBC and nucleus in each database. Figure 5 shows the histogram of the ratio of WBC and nucleus size for four databases. In BCCD (Mooney, 2019), ALL-IDB2 (Labati, Piuri, & Scotti, 2011), JTSC (Zheng, Wang, Wang, & Liu, 2018), and CellaVision (Zheng, Wang, Wang, & Liu, 2018) databases, most of the WBCs are within

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Algorithm 2: WBC Localization Method Applying row-wise operation to determine the number of WBC. Here rw is the variable, varying from 1 to row and row is the width of the image. for rw in row: 1. wp_cl_loc = Read the white pixel column location in rw. 2. Determine whether the rw is segmented or not from wp_cl_loc. 3. If it is segmented then count the segment number, seg_no and measure the length of each segmentation of rw. Otherwise, detect the rw as a single segment of white pixel and measure the length of it. 4. Detect the maximum length of the segmented white pixel row and store the row and column location of it, seg_loc (seg_no). end

Now measure the location of WBC based on the nucleus location. for sn in seg_no: 5. cl_loc = Read column location from seg_loc (seg_no). 6. cl_loc_img = image matrix of seg_nuc_i from cl_loc(1) to cl_loc(2) of all rows. The matrix size is row×(cl_loc(2)- cl_loc(1)). 7. seg_rw_len = Measure the length of the white pixel of each row of cl_loc_img. The size of this vector is row×1. 8. row_loc_max_len = Find the row location of the maximum values of the seg_rw_len. 9. Find the zero white pixel row location from location row_loc_max_len in the upward and downward directions of seg_rw_len. Assign the upward row location as rw_loc_up and downward row location as rw_loc_dw, respectively. 10. rw_loc_img = image matrix of seg_nuc_i from rw_loc_up to rw_loc_dw of all columns. 11. In the same way of step 7, 8 and 9 (column direction), measure the left column location (cl_loc_lf) and right column location (cl_loc_rg), respectively. 12. rw_loc_c = rw_loc_up + (rw_loc_down - rw_loc_up))/2. 13. cl_loc_c = cl_loc_lf + (cl_loc_rg - cl_loc_lf))/2; 14. max_sz = measure the maximum size between rw_loc_dw-rw_loc_up and cl_loc_rg-cl_loc_lf. 15. max_sz = max_sz + (max_sz×0.5). 16. wbc_rw_loc = [rw_loc_c - (max_sz/2), rw_loc_c + (max_sz/2)]. 17. wbc_cl_loc = [cl_loc_c - (max_sz/2), cl_loc_c + (max_sz/2)]. end

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the ratio of 1~1.5, shown in Fig. 5(a)-(d). For this reason, we localize the WBC including cytoplasm and nucleus into a boundary which is 1.5 times of the boundary of WBC nucleus. We implement this idea to determine the location of WBC from step 12 to 17 in Algorithm 2. Fig. 6(c) shows the WBC localization output of Algorithm 2.

(a) (b) (c) (d) Figure 4. Diversity of cytoplasm shape and color for (a) BCCD, (b) ALL-IDB2, (c) JTSC, and (d) CellaVision database.

(a) (b) (c) (d) Figure 5. Histogram of ratio (WBC:Nucleus) of four public databases. (a) BCCD, (b) ALL-IDB2, (c) JTSC, and (d) CellaVision.

(a) (b) (c) Figure 6. Output of Algorithm 2. (a) Input WBC image (Neutrophil and Monocyte), (b) Nucleus segmentation, and (c) Output of localized WBC.

3.3. Proposed CNN Model In our proposed CNN model, we use five convolutional layers (Conv Layer1, Conv Layer2, Conv Layer3, Conv Layer4, and Conv Layer5), three overlapping average-pooling layers (AvgPool11, AvgPool12, and AvgPool5), and two fully connected layers, which means one hidden layer and one output layer. Figure 7 shows the proposed CNN model. We normalize the range of RGB image from 0–255 to 0–1. The number of filters for each convolutional layer is the same (32), and the padding is

17

zero. As the CNN model computational complexity, training and testing time depend on the number and size of filters, and layers, we set optimum number of filters empirically by analyzing the works of (Chollet, 2016; He, Zhang, Ren, & Sun, 2016; Huang, Liu, Maaten, & Weinberger, 2017; Krizhevsky, Sutskever, & Hinton, 2017; Liang, Hong, Xie, & Zheng, 2018; Pang, Du, Orgun, & Yu, 2019; Simonyan & Zisserman, 2014; Szegedy et al., 2015a; Szegedy et al., 2015b). To construct the 1st, 2nd, 3rd, 4th, and 5th convolutional layers, we used filter sizes of 2×2, 2×2, 2×2, 3×3, and 6×6 and stride sizes of 2×2, 1×1, 1×1, 1×1, and 2×2, respectively. Inspired by (Iandola et al., 2016; Szegedy et al., 2015a; Simonyan & Zisserman, 2014; He, Zhang, Ren, & Sun, 2016; and Lin, Chen, & Yan, 2013), these filter sizes were selected for the convolutional layers. In (Pang, Du, Orgun, & Yu, 2019), they brought the concept of using the 1st and last convolutional layer features to the fully connected layer to provide more detailed local features. After the 1st convolutional layer, we use two overlapping average-pooling layers (AvgPool11, AvgPool12) and after the 5th

Figure 7. Proposed CNN model.

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convolutional layer, we use one overlapping average-pooling layer (AvgPool5). We fuse Conv Layer1, Conv Layer3, and Conv Layer4 with AvgPoolIn1, AvgPoolIn2, and AvgPoolIn3, respectively. The AvgPoolIn1, AvgPoolIn2, and AvgPoolIn3 are produced for the fusion purpose and they ensure the flow of original information to the convolutional layers, because they are similar to input image. Generally, the convolutional layers are stacked from top to bottom and only the first one is formed directly from the input image. However, we think that the other convolutional layers (other than the first one) may lose the originality of information. We understand this fact from the DenseNet (Huang, Liu, Maaten, & Weinberger, 2017) architecture. They describe it as layer utilization, and we follow it as input image utilization. Then, we concatenate the two overlapping average-pooling layers (AvgPool12 and AvgPool5) of the 1st and 5th convolutional layers to form the final fused layer to be fed into the fully connected layer (hidden layer). In the hidden layer, we use 128 neurons, and in the output layer we use four neurons. The LeakyReLU activation function is applied to all convolutional layers and the fully connected layer. The coefficient of the Leaky-ReLU activation function is set as 0.1 according to (Maas, Hannun, & Ng, 2013). It is determined experimentally. We use the softmax activation function in the output layer. We use a dropout layer on AvgPool12 and the hidden layer, at the rate of 0.3 and 0.1, respectively. The dropout rate is chosen experimentally. In (Srivastava et al., 2014), dropout is described as the solution of the overfitting problem. During training, a random number of neurons are eliminated according to the dropout rate and during testing, the rate is multiplied by the all the weights of the corresponding neurons of those layers. Equation (1) represents the convolutional operation for each convolutional layer.

 n i t , x  m  t  Lpf  y      W fp  i   Lcp 1  x     b f  c 1 i 1, x  m 

(1)

where Lpf  y  denotes the pixel of p th convolutional layer at location y for the f th filter. Lcp 1  x  denotes the pixel of the previous layer of p th convolutional layer at location x for the c th channel, where m is the starting pixel corresponding to the i th element of filter W fp and

19

n is the total number of channels. In Eq. (1), W fp  i  is the value of the f th filter of the p th convolutional layer at location i , t is the total elements of filter W fp , and b f is the bias term of the f th filter. For our model, we set the number of filters ( f ) for each convolutional layer as 32. Equation (2) represents the operation of Leaky-ReLU activation function.

Fcp  y   max  Lcp  y  , Lcp  y   0.1 where Fc

p

 y

(2)

refers the pixel after applying the operation of the Leaky-ReLU activation function

at location y for the c th channel of the p th convolutional layer. The average-pooling layer is expressed in Eq. (3).

Acp  y  

1 ph pw

x  ph pw

 x 1

Fcp  x 

(3)

where Acp  y  indicates the pixel at location y after applying the operation of average-pooling in the p th convolutional layer for the c th channel, where ph and pw denote the image patch height and width, respectively. From Eqs. (4) - (7), we describe the operation of a fully connected layer and an output layer. N

I n    fi  Wi , n   bn

(4)

H n  max  I n , I n  0.1

(5)

i 1

T

I k    H n  Wn, k   bk n 1

Pk 

(6)

exp  I k  4

 exp  I  k 1

(7)

k

After concatenation of the average-pooling layers, the features are in matrix format. We make them a single-vector format, and they are the inputs to the fully connected layer. In our proposed model, we use one fully connected layer (hidden layer). In Eq. (4), f i is the input feature vector, Wi , n is the weight from the input feature to the nth hidden layer neuron, and bn is the bias term of nth hidden layer neuron. Meanwhile, I n and N indicate the input to the nth hidden layer neuron

20

and the total number of input features, respectively. In Eq. (5), H n is the hidden layer output of the nth neuron after the operation of the Leaky-ReLU activation function. In Eq. (6), I k is the input

to the k th neuron in the output layer. We have to predict four types of WBC, so in the output layer, the total number of output neurons is four. In this case, Wn , k , bk , and T mean the weight from the nth neuron (hidden layer) to the k th neuron (output layer), bias term of the k th neuron in the output layer, and the total number of neurons in the hidden layer, respectively. We consider 128 hidden layer neurons ( T ). In Eq. (7), Pk is the softmax output of the k th neuron in the output layer.

3.4. Training of the Proposed CNN Model To train the proposed CNN model, we used the Adam (Kingma & Ba, 2014; Reddi, Kale, & Kumar, 2018) optimizer at a learning rate of 0.0001 with batch size of 8 and used the default parameters of the Keras framework. Adam is generally used for its fair robustness in the choice of hyper-parameters, but sometimes the learning rate needs to be changed from the suggested default value (0.001) (Goodfellow, Bengio, & Courville, 2017). Before training, we increased our dataset by rotating the WBC images at different angles. Although image rotation can downgrade the image quality (Liang, Hong, Xie, & Zheng, 2018), it does not change the normal characteristics of a cell. We did not scale or use any color transformation because size and color are the fundamental features of WBC images. Equation (8) represents the loss function.

E



1 B 4 Okz  log  Pkz   B z 1 k 1



(8)

where E and B indicate the loss function and the number of samples (mini-batch). In Eq. (8), Okz is the original output of the z th sample of the k th class (output layer neuron) and the value

of Okz is binary (0 or 1). Pkz is the predicted output (described as the softmax output of output layer neuron in Eq. (7)) of the z th sample of the k th class and the range of Pkz is (0, 1).

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4. Description of the Databases For the purpose of WBC nucleus segmentation, we use four public databases: BCCD (Mooney, 2019), ALL-IDB2 (Labati, Piuri, & Scotti, 2011), JTSC (Zheng, Wang, Wang, & Liu, 2018), and CellaVision (Zheng, Wang, Wang, & Liu, 2018). In this section, we describe these four databases which are used in our manuscript. The BCCD database is collected from (Mooney, 2019). In the database, there are two types of image sets. One set is without augmentation; it consists of 367 images with a resolution of 640×480. The images are obtained by using the Gismo Right technique with a regular light microscope with a 100x objective lens which is connected to an analog CCD color camera (Mohamed, Far, & Guaily, 2012). The other set is with augmentation (random rotation); it consists of 12,444 images with a resolution of 320×240. All of the images are color images. The augmentation is necessary because the dataset is imbalanced, which can influence the model during training (Qin et al., 2018). The augmentation is performed by random rotations, horizontal shifts, vertical shifts, and horizontal flipping. The WBC images are labeled in BCCD database and they are four types: Eosinophil, Neutrophil, Lymphocyte, and Monocyte. The images of ALL-IDB2 database were captured by a PowerShot G5 camera with a resolution of 2592×1944 and stored in JPG format with 24-bit color depth (Labati, Piuri, & Scotti, 2011). In this database, all WBC images are lymphocytes and the number of images are 260. JTSC database consists of 300 RGB WBC images with the resolution of 120×120 obtained from Jiangxi Tecom Science Corporation, China (Zheng, Wang, Wang, & Liu, 2018). The images were captured by a Motic Moticam Pro 252A optical microscope camera with a N800-D motorized auto-focus microscope and stored in 24-bit color depth BMP format (Zheng, Wang, Wang, & Liu, 2018). A newly-developed hematology reagent was used to process the blood smears for rapid WBC staining (Zheng, Wang, Wang, & Liu, 2018). CellaVision database consists of 100 RGB 300×300 color images which were collected from CellaVision blog (http://blog.cellavision.com) (Zheng, Wang, Wang, & Liu, 2018). ALL-IDB2, JTSC, and CellaVision databases are designed for the evaluation of segmentation algorithm (Labati, Piuri, & Scotti, 2011; Zheng, Wang, Wang, & Liu, 2018). The

22

images of JTSC and CellaVision databases are not labeled by expert hematologists (Zheng, 2019). For this reason, we do not use their databases for developing and evaluating the proposed CNN model. Therefore, we use these four databases for evaluating our proposed segmentation method. As mentioned above, only BCCD database has labeled WBCs, so we use only this database for the development and evaluation of our proposed CNN model based classification method.

5. Performance Evaluation and Comparison The performance of the proposed segmentation method is evaluated by accuracy (A), precision (P) (Olson & Delen, 2008), recall (R) or true positive rate (TPR) (Olson & Delen, 2008), specificity (S) (Andrade et al., 2019), Cohen‟s kappa index (K) (Cohen, 1960), and average dice coefficient (ADC) (Andrade et al., 2019). For evaluating the proposed classification method, we use the above metrics with excluding ADC and including F1-score (F1) (Sasaki, 2007), confusion matrix, area under the curve (AUC) of the receiver operating characteristic (ROC) curve (Zając, 2013), and the AUC of the precision–recall curve (Raghavan, Bollmann, & Jung, 1989; Manning & Schutze, 1999). We divided our BCCD database into two parts: 80% of the data (10,253 images) for training and 20% (2,558 images) for testing. 5.1. Description of Evaluation Metrics To express the metrics mathematically, we need to define the true positive (TP), false positive (FP), true negative (TN), and false negative (FN). TP is defined as the positive output for the corresponding positive ground truth (GT). FP is defined as the positive output for the corresponding negative GT. TN means the negative output for the corresponding negative GT. FN means the negative output for the corresponding positive GT. Our target is to segment background and WBC nucleus. So, in this case, WBC nucleus and background refer as positive and negative, respectively. With respect to classification, target class refers as positive class and others refer as negative class. The mathematical expressions of P, R, A, S, F1, K, ADC, and TPR are shown from Eqs. (9) - (18).

23

A

P

TP 100 TP  FP

(9)

R

TP 100 TP  FN

(10)

TP  TN 100 TP  TN  FP  FN

(11)

TN 100 TN  FP

(12)

S

P R P   R   K 1 2 1  2

F1  2 

2 

(13) (14)

TP  FN   TP  FP   TN  FN   TN  FP   TP  TN  FP  FN  DCl 

2

2  TP

 2  TP  FP  FN 

 n  ADC    DCl  / n  l 1  TPRt  #  w 100 / n, where l  w and DCl  t

(15) (16) (17) (18)

Equation (9) represents precision (P) that indicates the percentage of correctly predicted positive samples (TP) over the total number of predicted positive samples (TP+FP). Recall (R) is represented by Eq. (10) that indicates the percentage of correctly predicted positive samples (TP) over the total number of positive samples (TP+FN). In Eq. (11), A represents the percentage of correctly predicted positive and negative samples (TP+TN) over the total number of samples (TP+TN+FP+FN). Specificity (S) expressed in Eq. (12) indicates the percentage of correctly predicted negative samples (TN) over the total number of negative samples (TN+FP). P, R, A, and S vary from 0 to 100. The best and worst value of them are 100 and 0, respectively. In Eq. (13), P and R indicate the precision and recall value, respectively. The range of F1 score is from 0 to 1. Higher F1 score represents higher quality of the method. Equation (14) shows Cohen‟s kappa index, K while θ1=A and θ 2 is expressed in Eq. (15). The performance quality can be defined by the score of K as follows: poor (K≤0.2), reasonable (0.20.8) (Andrade et al., 2019). In Eq. (16), DCl represents Dice coefficient (DC) for

24

each l segmented WBC. ADC is the average of DCl for a database with n WBCs, as shown in Eq. (17). DC and ADC vary from 0 to 1 and the more it will be, the better the method is. When the value of DC exceeds a specific threshold (t), then it refers a segmentation method as a good method (Andrade et al., 2019). In this paper, according to (Andrade et al., 2019), TPRt is defined as the ratio of the number of WBCs (w) with DCl  t to the total number of WBCs (n) of a database, shown in Eq. (18). According to (Andrade et al., 2019), we set the threshold (t) of DC as 0.9 to measure TPRt . The value of TPRt is varied from 0 to 100. Higher value of it represents better quality of the method. The AUC of the ROC is a performance measurement for the classification problem at various threshold settings for plotting the TPR or recall vs the false positive rate (FPR). The FPR is expressed as FP/(TN+FP). For plotting precision vs recall, we measured the precision and recall for various thresholds settings. The best and worst values of the AUC of ROC and precision–recall curve are same, 1 and 0, respectively. The top left corner of the ROC curve and the top right corner of the precision–recall curve show better performance of the model. 5.2. Results and Comparison of the Proposed Methods For our simulation and all experiments, we used the Windows 10 operating system, 8 GB RAM, an Intel Core-i7 processor, and an NVIDIA GPU GeForce GTX 1050 with 4 GB RAM. We used the GPU for the training of each CNN model. By measuring the value of evaluation metrics, we compare our proposed WBC nucleus segmentation method with four methods (Nasir, Mashor, & Rosline, 2011; Nazlibilek et al., 2014; Vogado et al., 2016, and Kumar & Vasuki, 2017) visually and quantitatively. We select four nucleus segmentation methods based on the analysis of nucleus segmentation works of (Andrade et al., 2019) because the four nucleus segmentation methods have superior performance at least 2 quality metrics or at least 2 public databases. We execute their segmentation methods on MATLAB using our above mentioned embedded hardware configuration. For quantitative evaluation of our proposed WBC nucleus segmentation method, we use seven quality metrics. We compare our proposed CNN model with four state-of-the-art CNN models (fused CNN (Pang, Du, Orgun, & Yu, 2019), CNN-RNN (Liang, Hong, Xie, & Zheng, 2018), (Yu et

25

al., 2017), and (Banik, Saha, & Kim, 2019)) which were developed for biomedical image classification application. In this case, we use nine evaluation metrics. For fair comparison, after applying our proposed WBC nucleus segmentation and localization algorithm, we feed the WBCs to train each CNN model on BCCD database by using same configuration of personal computer as mentioned above. We measure and compare the training time, value of nine evaluation metrics and per image processing time for each CNN model. To train for 100 epochs, the fused CNN model, CNN–RNN model, Yu et al. model, Banik, Saha, and Kim model, and our proposed CNN model take approximately 195, 520, 330, 38, and 35 minutes, respectively. We used the Adam optimizer, a batch size of 8, and a learning rate of 0.0001 to train the four state-of-the-art CNN models. Figure 8 shows the visual comparison of our proposed WBC nucleus segmentation method with four methods, (Nasir, Mashor, & Rosline, 2011; Nazlibilek et al., 2014; Vogado et al., 2016; Kumar & Vasuki, 2017). Same kind of segmentation result is also visualized for (Nazlibilek et al., 2014) and (Kumar & Vasuki, 2017) method except ALL-IDB2 database, shown in Figs. 8a(iii), c(iii), d(iii), and Figs. 8a(v), c(v), d(v), respectively. But our WBC nucleus segmentation output is more pleasant than (Vogado et al., 2016) method and also quite similar to ground truth, shown in Figs. 8a(vi)-d(vi), and 8a(iv)-d(iv). The output images of (Nasir, Mashor, & Rosline, 2011) are quite satisfactory in all databases except CellaVision database, as shown in Fig. 8a(ii)-d(ii). In Fig. 8d(ii), we can see that (Nasir, Mashor, & Rosline, 2011) method can segment only single part of the whole nucleus which is not expected. For quantitative analysis, we have listed the comparison of our proposed WBC nucleus segmentation method with four methods (Nasir, Mashor, & Rosline, 2011; Nazlibilek et al., 2014; Vogado et al., 2016; Kumar & Vasuki, 2017) from Table 2 to 5 for BCCD, ALL-IDB2, JTSC, and CellaVision databases. The best value is marked in bold font. In terms of accuracy, kappa index, ADC, and TPR0.9, our proposed WBC nucleus segmentation method achieves 1st rank for four public databases, as shown from Table 2 to 5. Although our proposed nucleus segmentation method cannot achieve the highest score in precision (P), recall (R), and specificity (S) quality metrics for

26

four public databases, our segmentation method cannot affect the accuracy or performance of our classification method. Because after segmentation method, we apply WBC localization method based on the location of detected nucleus to crop the WBC in cell region, described in algorithm-2. It ensures the feeding of whole WBC nucleus into the CNN model. For this reason, the performance of our segmentation method on BCCD database cannot influence the performance of our CNN model. Thus, our proposed WBC localization method removes the performance dependency between our segmentation and classification method. Although all four methods (Nasir, Mashor, & Rosline, 2011; Nazlibilek et al., 2014; Vogado et al., 2016; and Kumar & Vasuki, 2017) use color space conversion, and the final decision of WBC nucleus segmentation was taken based on the specific component of color space while our WBC nucleus segmentation method does not depend on any specific component of color space. For this reason, from the analysis of visual and quantitative comparison, we can claim that our proposed WBC nucleus segmentation method is a generalized method with respect to input WBC image color which is also proved by showing the superior score of the evaluation metrics from Table 2 to 5, marked in bold font. In the same way, we can also claim that our proposed WBC nucleus segmentation is quite robust in terms of image resolution and color because image resolution and color are varied into each database as described in section 4 and also shown in Fig. 4. Table 2: Comparison of our proposed WBC nucleus segmentation method on BCCD database. Methods A (%) (Nazlibilek et al., 2014) 85.79 (Vogado et al., 2016) 99.15 (Kumar & Vasuki, 2017) 75.88 (Nasir, Mashor, & Rosline, 2011) 99.22 Proposed method 99.42

P (%) 15.81 80.51 3.86 87.77 94.38

R (%) 51.46 94.51 77.76 89.77 91.21

S (%) 86.64 99.30 14.17 99.59 99.78

K 0.19 0.86 0.01 0.88 0.92

ADC TPR0.9 (%) 0.20 0 0.86 28.06 0.06 0 0.88 43.00 0.92 80.10

Table 3: Comparison of our proposed WBC nucleus segmentation method on ALL-IDB2 database. Methods

A (%) P (%) R (%) S (%)

(Nazlibilek et al., 2014) 95.80 (Vogado et al., 2016) 98.59 (Kumar & Vasuki, 2017) 98.54 (Nasir, Mashor, & Rosline, 2011) 97.81 Proposed method 98.61

80.09 91.24 91.89 96.00 96.35

89.27 98.09 98.73 88.70 93.80

96.05 98.62 97.10 99.48 99.33

K 0.81 0.93 0.93 0.90 0.93

ADC TPR0.9 (%) 0.83 0.94 0.94 0.91 0.94

70.00 87.60 89.60 73.07 91.15

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Table 4: Comparison of our proposed WBC nucleus segmentation method on JTSC database. Methods

A (%) P (%) R (%) S (%)

(Nazlibilek et al., 2014) 87.23 (Vogado et al., 2016) 97.13 (Kumar & Vasuki, 2017) 96.77 (Nasir, Mashor, & Rosline, 2011) 91.20 Proposed method 97.57

53.10 93.55 93.43 96.53 87.63

85.63 98.99 98.97 99.95 96.08

98.68 83.18 81.86 28.08 97.92

K 0.60 0.86 0.84 0.37 0.89

ADC TPR0.9 (%) 0.67 0.87 0.86 0.40 0.91

11.66 52.33 46.66 1.00 73.66

Table 5: Comparison of our proposed WBC nucleus segmentation method on CellaVision database. Methods

A (%) P (%) R (%) S (%)

(Nazlibilek et al., 2014) 84.03 (Vogado et al., 2016) 98.77 (Kumar & Vasuki, 2017) 96.60 (Nasir, Mashor, & Rosline, 2011) 93.17 Proposed method 98.86

52.09 97.88 94.08 82.35 91.75

82.59 99.75 99.21 99.78 98.09

94.21 89.39 74.55 30.95 98.98

K 0.54 0.92 0.80 0.36 0.93

ADC TPR0.9 (%) 0.61 0.93 0.82 0.38 0.94

21.00 87.00 41.00 15.00 90.00

a(i)

a(ii)

a(iii)

a(iv)

a(v)

a(vi)

a(vii)

b(i)

b(ii)

b(iii)

b(iv)

b(v)

b(vi)

b(vii)

c(i)

c(ii)

c(iii)

c(iv)

c(v)

c(vi)

c(vii)

d(i) d(ii) d(iii) d(iv) d(v) d(vi) d(vii) Figure 8. Visual comparison of the output of proposed WBC nucleus segmentation method with the output of (Nasir, Mashor, & Rosline, 2011), (Nazlibilek et al., 2014), (Vogado et al., 2016), and (Kumar & Vasuki, 2017) for database a(i) - a(vii) BCCD, b(i) - b(vii) ALL-IDB2, c(i) - c(vii) JTSC, and d(i) - d(vii) CellaVision. Red color indicates the segmented WBC nucleus. In each row, the images are arranged in this order: (i) input, WBC nucleus segmentation output of (ii) (Nasir, Mashor, & Rosline, 2011), (iii) (Nazlibilek et al., 2014), (iv) (Vogado et al., 2016), (v) (Kumar & Vasuki, 2017), (vi) proposed WBC nucleus segmentation method, and (vii) ground truth.

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WBCs can be cropped wrongly while the segmentation method will fail to segment nucleus deliberately. From Table 2 to 5, we list our segmentation evaluation result with different quality metrics on four public databases. We achieve remarkable score for our segmentation method for all databases in every quality metrics. Therefore, we can say that our segmentation method will not fail to segment nucleus deliberately. After segmentation, we apply WBC localization method to crop the WBC. If our segmentation method fails to segment any part of the nucleus, it can be recovered

through covering the cell region while we apply our proposed WBC localization method. For this reason, because of the WBC localization method, we do not have any chance to crop the WBC wrongly. So, there is no wrongly cropped WBCs during the training and testing of our CNN model. Figures 9 and 10 show a comparison of training and testing curves among the four state-of-the-art CNN models and our proposed CNN model. Our proposed CNN model trained faster because it achieved a maximum test accuracy of 96% at 35th epoch, as shown in Fig. 10. From Fig. 10, we also can say that our testing curve is less noisy than the other four CNN models. This implies that our CNN model is confronted less by the overfitting problem than the four CNN models. In Fig. 11 and Fig. 12, we show the training and testing curves of random training and validation set of BCCD database. The characteristics of loss and accuracy curves are quite same to each other, as can be seen in Fig. 11 and Fig. 12. This implies that our proposed CNN model is independent for any kind of random data selection from BCCD database. In Table 6, we show the confusion matrix of our proposed CNN model where highest accuracy 96% is achieved at 35th epoch on BCCD test database, in which the label of the WBC class is expressed by the first letter of WBC class name. From this table, we can see that our model makes the maximum confusion between the predictions of Neutrophil and Eosinophil-type WBC. But the confusion percentage is very small, about 8.5% (for Neutrophil class). In Table 7, we show the values of six classification metrics, the processing time per image, and the number of trainable parameters for comparing our CNN model with four state-of-the-art CNN models (Liang, Hong, Xie, & Zheng, 2018; Pang, Du, Orgun, & Yu, 2019; Yu et al., 2017; and

29

Figure 9. Comparison of the training curves between our proposed CNN model and four CNN models.

Figure 10. Comparison of the testing curves between our proposed CNN model and four CNN models.

Figure 11. Training curve of the proposed CNN model for random training set of BCCD database.

30

Figure 12. Testing curve of the proposed CNN model for random test set of BCCD database. Table 6: Confusion matrix of our proposed CNN model on the BCCD test database (Highest accuracy 96% is achieved at 35th epoch on BCCD test database.).

True Label

N N 615 E 42 M 5 0 L

Predicted Label E M 48 8 594 0 0 618 0 0

L 1 0 1 626

Total 672 636 624 626

Banik, Saha, & Kim, 2019). The per-image processing time is measured through the CPU configuration (Windows 10 operating system, 12-GB RAM, and Intel Core-i7 processor, tensorflow 1.13.2). We use bold font to identify the maximum value of each classification metric. In most of the classification metrics for each WBC class, our CNN model achieves the best value than other CNN models (Pang, Du, Orgun, & Yu, 2019; Liang, Hong, Xie, & Zheng, 2018; Yu et al., 2017; and Banik, Saha, & Kim, 2019) because the quality scores of all metrics are more than 90% where other CNN models cannot achieve scores more than 90% in all quality metrics. The metrics in which our CNN model does not achieve 1st rank, has average 1.57% difference from the highest score of other CNN models. It implies the satisfactory performance of our CNN model. From the kappa value and F1-score, it is proved that the overall classification performance of our CNN model is better than other CNN models which also brings the credibility to our proposed CNN model. For our CNN model, per image inference time and number of parameters are the lowest and for this reason, hematologists can use our CNN model based device into their diagnosis system for fast inference.

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We also show the micro-averaged ROC and precision–recall curve to compare the four CNN models (Liang, Hong, Xie, & Zheng, 2018; Pang, Du, Orgun, & Yu, 2019; Yu et al., 2017; and Banik, Saha, & Kim, 2019) with our proposed CNN model in Figs. 13 and 14, respectively. The AUC of ROC indicates the confidence level of correct prediction of a CNN model. The AUC of precision–recall curve indicates the confidence level of predicting true positive and false positive classes. From Figs. 13 and 14, we can say that our proposed CNN model shows superior performance than four state-of-the-art CNN models (Liang, Hong, Xie, & Zheng, 2018; Pang, Du, Orgun, & Yu, 2019; Yu et al., 2017; and Banik, Saha, & Kim, 2019). Besides, we can also claim that our proposed CNN model is more suitable for the application of medical diagnosis systems than the four state-of-the-art CNN models (Liang, Hong, Xie, & Zheng, 2018; Pang, Du, Orgun, & Yu, 2019; Yu et al., 2017; and Banik, Saha, & Kim, 2019). Table 7: Comparison between our proposed CNN model and four state-of-the-art CNN models (Liang, Hong, Xie, & Zheng, 2018; Pang, Du, Orgun, & Yu, 2019; Yu et al., 2017; and Banik, Saha, & Kim, 2019) on BCCD test database. Methods Fused CNN (Pang, Du, Orgun, & Yu, 2019) CNN–RNN (Liang, Hong, Xie, & Zheng, 2018)

(Yu et al., 2017)

(Banik, Saha, & Kim, 2019)

Our proposed CNN

WBC Cell P (%) R (%) S (%) A (%) Type N

F1

71

95

86.37

88.66

0.82

E

94

84

98.28

94.80

0.89

M

100 100 78 93 96 100 68

75

100 100 90.88 97.86 98.96 99.94 85.20

93.86

0.86

100 91.08 96.24 94.44 99.92 85.53

1.00 0.84 0.92 0.88 1.00 0.76

93.18 96.58 97.87 88.51 98.87 100 99.78

88.19 88.42 97.42 90.55 95.05 95.33 99.84

93

97.50 97.50

95.93 96.48

0.75 0.73 0.95 0.84 0.89 0.90 1.00 0.92 0.93

99 100

99.58 99.89

99.45 99.92

0.99 1.00

L N E M L N E M L N E M L N E

78 86 94 74 96 100 99

M L

99 100

93 93

100 92 91 80 100 86 73 63 96 97 84 81 100 92

K

Time Per Image (Second)

Number of Parameters (Million)

0.84

0.1640

27.215

0.87

0.2191

59.621

0.73

0.1706

113.645

0.87

0.4109

1.202

0.94

0.1475

0.133

32

Figure 13. Comparison of the ROC curves and AUCs of ROC between our proposed CNN model and four CNN models.

Figure 14. Comparison of the precision–recall curves and AUCs of precision–recall between our proposed CNN model and four CNN models.

6. Conclusion The metrics-based evaluation, analysis, and comparison with other methods prove the superior performance of our proposed WBC nucleus segmentation, and CNN model based WBC classification method on four public databases. Behind the success of our proposed methods, some notable aspects play a vital role. First, HSI, L*a*b color space, and k-means algorithm-based WBC

33

nucleus segmentation method increase the generalization capability and evaluation result with higher score on quality metrics ensures the contribution of it. Secondly, WBC nucleus based WBC localization procedure are the key factor to reduce the performance dependency between proposed nucleus segmentation and classification method. And for this reason, we successfully confirm that segmentation performance does not affect the accuracy of the proposed classification method. Finally, the development of a new CNN model is another important criterion. The idea of fusing the features of the first and last convolutional layers and the propagation of input images to the convolutional layers ensures better accuracy than the other four state-of-the-art CNN models. Because of the lower number of trainable parameters of the proposed CNN model, it trains faster than the four state-of-the-art CNN models. Besides, our proposed CNN model has considerably less computational complexity than the four state-of-the-art CNN models. It brings the feasibility to the hematologists into their diagnosis system. At last, we can claim that our proposed WBC nucleus segmentation and classification methods breakdown many limitations of state-of-the-art methods and increases the possibility of acceptability to the hematologists for the real-time use in the diagnosis scope. In the future, we will perform the analysis of additive noise on input WBC image. For this purpose, we will need a generalized pre-processing stage which can detect the type of noise and can process according to the type of noise. Therefore, in future, we will try to propose a generalized preprocessing stage that will de-noise and enhance the visibility of the input image before step forward to the post-processing stage. We will also focus for reducing the confusion rate of Eosinophil and Neutrophil. Although our proposed segmentation method ranks 1st in TPR0.9, the score of TPR0.9 is less than 90% for BCCD and JTSC databases. The reason of the contrast variation of BCCD and JTSC database. Pre-processing stage may help the proposed segmentation method to improve the score of TPR0.9. For this reason, in future, we will improve our segmentation method and propose a generalized pre-processing stage for the application of differential WBC counting to increase the generalization capability. In the future, we will also try to establish a general CNN model to classify

34

different types (around 40) of WBCs originating from red bone marrow, which is vital for leukemia recognition.

Author Contribution Statement Partha Pratim Banik: Conceptualization, Formal analysis, Methodology, Software, WritingOriginal draft preparation, Visualization, Investigation. Rappy Saha: Data curation, Validation, Writing- review and editing. Ki-Doo Kim: Writing- review and editing, Supervision, Project Administration, Funding Acquisition. . Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (NRF-2019R1F1A1062317) and was also supported by the National Research Foundation of Korea Grant funded by the Ministry of Science, ICT, Future Planning [2015R1A5A7037615].

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