Computer-aided diagnosis of cataract using deep transfer learning

Biomedical Signal Processing and Control 53 (2019) 101533

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Computer-aided diagnosis of cataract using deep transfer learning Turimerla Pratap ∗ , Priyanka Kokil Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai 600127, India

a r t i c l e

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Article history: Received 5 October 2018 Received in revised form 23 March 2019 Accepted 13 April 2019 Keywords: Computer-aided diagnosis Pre-trained convolutional neural networks Feature extraction Support vector machine classifier Transfer learning

a b s t r a c t Cataract is a leading eye disease across the world. If cataract is not diagnosed in earlier stage, then it may lead to blindness. Earlier detection is the best way to control the risk and to avoid painful surgery. Thus, this paper mainly focuses on cataract detection from fundus retinal images. A computer-aided automatic cataract detection method is proposed to detect various stages of the cataract such as normal, mild, moderate, and severe from the fundus images. The proposed method uses the pre-trained convolutional neural network (CNN) for the transfer learning to carry out automatic cataract classification. Then pretrained CNN model is used for the feature extraction and the extracted features are then applied to a support vector machine (SVM) classifier. The fundus cataract images are collected from the various open access datasets and labelled into four stages with the help of ophthalmologic experts. The four stage classification accuracy obtained is 92.91%. Since, the image quality is important in CNN, an image quality selection module is incorporated to decide the quality of fundus image for diagnosis. The revaluation of results based on the quality of fundus images is also presented. Based on the results, the proposed method proved to be an efficient method that uses pre-trained CNN as transfer learning for the classification of the cataract. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction Eye vision is an important sense to every human being. But unfortunately, many people across the world are suffering from vision impairments. As per world health organization (WHO) 2010 report, 285 million people are suffering from vision impairments worldwide, out of which 39 million are blind and 246 million are having moderate to severe vision impairments (MSVI) [1]. According to the international agency for prevention of blindness (IAPB) 2015 report, over 7.3 billion populations worldwide, 253 million people are suffering from vision impairments out of which 36 million are blind and 217 million are having MSVI [2]. As shown in Fig. 1(a), there is a little improvement in controlling the vision loss in past half a decade. Therefore, a global initiative program has been developed to control the vision loss called VISION 2020: a right to sight by WHO [3]. Ninety percent of the people who are affected by visual impairments belong to developing countries due to lack of medical facilities. In that, 75% of visual impairments are avoidable which means that approximately four out of five cases are curable [4]. In India, the total number of blind people is approximately 8 million. In that, 90% of the people belong to rural areas. The preva-

∗ Corresponding author. E-mail address: [email protected] (T. Pratap). https://doi.org/10.1016/j.bspc.2019.04.010 1746-8094/© 2019 Elsevier Ltd. All rights reserved.

lence of blindness in India is 1.1%. As per our knowledge, India is the first country to initiate the preventive measures towards blindness. However, most of the ophthalmologists have little time to conduct blindness preventing surgeries because they are usually flooded with general eye check-ups. The removal of blindness due to cataract is possible by performing adequate number of surgeries by experienced ophthalmologists. However, conducting required number of cataract surgeries with the available ophthalmologists is still a challenge. The public awareness, limited accessibility, high cost of treatment, and poor surgical outcomes are the major challenges for the blindness prevention [2,5]. As shown in Fig. 1(b), the main causes of blindness are cataract (51%), uncorrected refractive error (URE) (3%), glaucoma (8%), agerelated macular degeneration (AMD) (5%), corneal opacity (4%), trachoma (3%), diabetic retinopathy (DR) (1%), and others (25%) [1,6]. Undoubtedly, cataract is a leading cause of blindness. Age, diabetes, smoking, etc. are common factors responsible for cataract [6]. If one can detect and diagnose the cataract effectively in its early stage, then cataract can be avoided and substantial benefits can be achieved in short period. Therefore, this develops a need for more research in the non-invasive computer-aided diagnosis of cataract. The fundus camera is an important imaging device to assess the condition of eye diseases. The use of a non-mydriatic fundus camera for obtaining fundus images in the diagnosis of cataract has been increased in recent years. The main reason for increased importance of fundus image is that, currently many handheld fundus imaging

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T. Pratap and P. Kokil / Biomedical Signal Processing and Control 53 (2019) 101533

Fig. 1. (a) Worldwide report on vision impairments from WHO 2010, 2015 data [1,2] (b) Global causes of vision impairments [2,6].

Fig. 2. Fundus images with different stages of cataract. (a) Normal (b) Mild (c) Moderate and (d) Severe.

Fig. 3. RGB componets of fundus image(a) R-channel (b) G-channel (c) B-channel.

tools have been developed, which can be potentially applied in rural area and large population screen [7]. Motivated by the preceding discussions, this paper mainly focuses on the classification of cataract from digital fundus images. In order to validate the proposed scheme, the fundus camera images affected by cataract have been collected from the various open access datasets. As shown in Fig. 2, the collected images are labelled into four different classes such as normal, mild, moderate and severe with the help of experienced ophthalmologists. The pre-trained convolution neural network (CNN) used as the feature extraction technique and the extracted features are applied to support vector machine (SVM) classifier. The organization of the remaining paper is as follows: Section 2 describes related works in cataract detection. Pre-processing of retinal images, automatic feature extraction using pre-trained CNN model and classification of features using SVM classifier is explained in Section 3. The experimental results are discussed in Section 4. Discussion and conclusions are presented in Sections 5 and 6, respectively.

2. Related works The analysis of fundus images for cataract detection has attracted the attention of many researchers in past several years

[8–16]. The cataract detection system mainly consists of three stages: pre-processing, feature extraction, and classification. Several results have been reported in literature for cataract detection using retinal images [9–16]. In pre-processing stage, original RGB retinal image is converted to G-channel in RGB colour space as it has most obvious contrast between object and background [17]. So, G-channel has been extensively used to improve the non-uniform illumination of fundus image [9–13,15,16]. The G-channel of colour fundus image is having better visibility as compared to other two (R- and B-) channels [17], as shown in Fig. 3. Automatic classification of retinal image for cataract detection has been studied in [9]. Along with G-filter pre-processing, the improved top-bottom hat transformation has been utilized to enhance the retinal image. Then the trilateral filter has been used to remove the noise [9]. The back propagation neural network (BPNN) has been used for the classification of cataract and accuracy obtained was 82.29% [9]. In [10], the authors have considered two-dimensional (2-D) discrete Fourier transform (DFT) features to detect the cataract. The dimensionality of the feature vector has been reduced by using principal component analysis (PCA). Linear discriminant analysis (LDA) classifier with the AdaBoost algorithm has been used to train and test the method. The accuracy of 81.52% was obtained by LDA and Adaboost algorithms [10].

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Fig. 4. Framework for transfer learning based computer-aided cataract detection.

A computer-aided diagnosis system for automatic cataract classification and grading by using fundus images has been developed in [11]. Authors have made use of discrete wavelet transform (DWT) and discrete cosine transform (DCT) for feature extraction. For classification, an elegant use of multi-class discriminant analysis algorithm has been made [11]. An ensemble learning based methodology is adopted to improve the cataract detection accuracy in [12]. Independent feature sets, namely, wavelet-, sketch-, and texture-based features were extracted. SVM and BPNN learning models have been built for final fundus image classification. For classification, majority voting and stacking were used. The performance in terms of correct classification rate obtained was 93.2% and 84.5% for cataract detection and grading, respectively, this also demonstrates the outperformance of the method given in [12] as compared to single learning models. In [13], the authors have proposed a cataract detection system by evaluating blurriness with vitreous opacity removal. The three types of features were extracted, namely, pixel number of visible structures, mean contrast between vessels and background, and local standard deviation by utilizing 2-D Gaussian filter. For classification of images into four grades, a decision tree has been used. To avoid the wrong detection of vitreous opacity as retinal structures, a morphological method has been used to detect and remove such lesions from retinal visible structure segmentation. Total 1355 images have been tested with this approach and accuracy obtained was 83.8% for four grade grading of blurriness. In [14], an elegant use of genetic algorithm (GA) to the segmented parts of the input image has been made. These GA features followed by SVM classifier resulted in 87.52% of classifier accuracy. The use of CNN in the computer-aided diagnosis for detection and grading of cataract in fundus images has been done in [15]. Feature maps at pool5 layer with their high-order empirical semantic has also been studied. It has been demonstrated that CNN outperforms when number of fundus images are large. However, collection of large number of real time datasets poses significant challenge. Furthermore, the pre-trained CNN is preferred to extract the features automatically from the medical images [18,19]. The use of singular value decomposition for the feature extraction and SVM classifier for classification for two class cataract detection has been done in [16]. However, still there is a lot of

scope to improve the accuracy which motivated us to carry out the present work. 3. Methodology In this section, the methodology of retinal image classification for cataract detection is explained. The entire methodology is mainly divided into four steps: image quality selection, preprocessing, feature extraction and classification. The framework for transfer learning based cataract detection is shown in Fig. 4. Training a CNN from scratch is usually slower and difficult than training a pre-trained network. Instead of training and testing the CNN with same natural images, transfer learning performs testing of medical images on pre-trained CNN which is already trained with millions of non-medical images [20]. Let X = {x(i) |i = 1, 2, . . ., M} represents the input space that contains all the feature vectors, where x(i) represents the n-D feature vector. Further, assume F be the input dataset that contains fundus images as F(1), F(2), . . ., F(M), where M is the total number of images in F. Let C = {c1 , c2 , . . . ck } is the finite set with k number of labels. In this paper, M is considered as 800 and k is 4. The dataset F can be represented as F = {(x(i) , y(i) )|x(i) ∈ Rn , y(i) ∈ C,

i = 1, 2, . . ., M},

(1)

y(i)

represents the corresponding label of an ith feature x(i) . where Next, divide the input dataset F into training dataset FT and testing dataset FV . In supervised machine learning, the main aim is to find a classifier f : X → C that minimizes the expected value of some loss function i.e., gˆ (x(i) ) = minL(f, FT ) + J(f ), f ∈H

(2)

where L is a loss function of the training dataset, H represents hypothesis space of models, J denotes the penalty term for the complexity of the model and  signifies a regularization parameter. The loss function can be written as L(f, FT ) =

M  i=1

L(f (x(i) ), y(i) ),

(3)

4

T. Pratap and P. Kokil / Biomedical Signal Processing and Control 53 (2019) 101533

Fig. 5. (a) Threshold based image quality selection. (b) Scatter plot of collected fundus images used for evaluation.

• The G-channel extraction is simple and effective. • The G-channel provides more details of luminance. • The processing time reduces by a factor of 1/3 of the actual time.

Fig. 6. Image quality selection method.

where L represents the loss between a label and a model prediction for a single instance. Then the accuracy of the model under consideration is calculated to evaluate the worthiness of the proposed method.

The performance of the proposed method with the G-channel of the input and histogram equalization of the G-channel of the input is evaluated. The histogram equalization is a good technique to improve the luminance conditions of an image. Since the Gchannel and the histogram equalization of the G-channel has given the same performance results, the histogram equalization method is neglected after the G-channel extraction. 3.3. Feature extraction

3.1. Fundus image quality selection In machine learning algorithms, the quality of an image plays an important role. Difference in the quality of images in training set and testing set leads to degradation in the performance of a classifier [21]. So, it is highly desirable to maintain the same image quality level in training and testing phase. In this paper, the image quality selection module is incorporated to filter out good quality fundus images for further diagnosis. The image quality of a fundus is assessed using no-reference image quality evaluators such as naturalness image quality evaluator (NIQE) [22] and perception based image quality evaluator (PIQE) [23]. The NIQE and PIQE acquire low score for good quality images and achieve high scores for poor quality images. From the experimental analysis, the maximum values of NIQE and PIQE for good quality images are found to be 5 and 50, respectively [22,23]. The NIQE and PIQE scores are calculated on the collected dataset of 800 fundus images and the corresponding scatter plot is shown in Fig. 5. The fundus images whose NIQE score is ≤5 and PIQE score is ≤50 are only used for training and testing phase. The proposed method uses P as threshold value. As shown in Fig. 6, the fundus images whose coordinate points falls under P are considered for diagnosis and remaining fundus images are rejected. 3.2. Pre-processing

The feature extraction stage is essential because of its impact on the efficiency of the classification system. The pre-trained CNN is used as a starting point to learn a new task. Fine-tuning of CNN network is simple and faster than training a network with randomly initialized weights from scratch [24,25]. In the proposed method, the pre-trained AlexNet model [26] is used. AlexNet model trained on an ImageNet database which can classify a million images into 1000 classes. This model contains eight layers: the first five (Conv.1, Conv.2, Conv.3, Conv.4, and Conv.5) are convolutional layers and the last three (fc6, fc7, fc8) are fully connected layers as shown in Fig. 7. The output of the fully connected layer fc7 is applied to the SVM classifier. The use of AlexNet is to carry out the transfer learning in the classification of fundus cataract images is proposed. This transfer learning achieves the better classification accuracy as compared to the existing methods [9–16]. 3.4. Classification In the classification stage, the SVM classifier [27] is selected for the better classification results. For a given feature vector x(i) , i=1, 2,. . .,M, the binary SVM requires solution of the following optimization problem:

 1 T w w+  (i) , w,b,i 2 M

G-channel is widely used a pre-processing technique in computerized retinal disease diagnosis systems. In the pre-processing stage, the G-channel extracted from the input RGB retinal image. The use of R-channel along with a G-channel is advantageous as its performance is inferior as compared to the G-channel alone. The importance of G-channel is shown experimentally in the later section. The advantages of the G-channel are as follows:

min

(4)

i=1

subject to y(i) (wT ϕ(x(i) ) + b) ≥ 1 −  (i) ,

(5)

 (i) > 0,

(6)

for i = 1, 2, . . .M,

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Fig. 7. Architecture of pre-trained AlexNet [26].

where w ∈ Rn is the weight vector, b represents threshold,  is regularization constant and the mapping function ϕ projects the training data into suitable feature space X. For multi-class classification problems, one versus one technique is adopted in which multiple binary class SVMs are combined to achieve multi-class SVM. This multi-class classification model is a subset of error-correcting output codes model [28]. 4. Experimental results This section presents the experimental results of the proposed method on the collected dataset. All the experiments were executed by Matlab R2018a in a personal computer with Intel(R) core(TM) i7-7500U processor, 8.00 GB RAM, and 64bit Windows10 operating system. The feature extraction and classification components are implemented with deep learning toolbox and statistics and machine learning toolbox, respectively in Matlab. The evaluation results and comparisons are reported exclusively. 4.1. Dataset A proper dataset is required for effective training of the classification model. All the cataract retinal images used in this study are selected from the various open access datasets, namely, high resolution fundus (HRF) image database [29], structured

Table 1 The overall dataset collected. Categories

Number of images

Normal Mild Moderate Severe

200 200 200 200

Total

800

Good quality

Poor quality

Training

Testing

Training

Testing

50 50 50 50

50 50 50 50

50 50 50 50

50 50 50 50

200

200

200

200

analysis of the retina (STARE) [30], standard diabetic retinopathy database (DIARETDB0) [31], e-ophtha: a color fundus image database [32], methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (MESSIDOR) database [33], digital retinal images for vessel extraction (DRIVE) database [34], fundus image registration (FIRE) dataset [35], digital retinal images for optic nerve segmentation database (DRIONS-DB) [36], Indian diabetic retinopathy image dataset (IDRiD) [37], available datasets from Dr. Hossein Rabbani [38–40], and other internet resources. As shown in Table 1, the overall collected dataset consists of 200 normal, 200 mild, 200 moderate and 200 severe stage cataract images. The main objective of the proposed method is to learn discriminative features that distinguish one class from the other.

Fig. 8. Comparison of training and testing accuracies for different classifiers evaluated on fundus images with G-filter.

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Fig. 9. Comparison of training and testing accuracies for different classifiers evaluated on fundus images without G-filter.

Table 2 Comparison of results with other methods. S. No

Reference

1

[9]

2

[10]

3

[11]

4

[12]

5 6 7 8 9

[13] [14] [15] [16] Proposed

Feature extraction

Classification

Two class accuracy (%)

Four class accuracy (%)

Luminance Gray co-occurrence matrix Gray gradient co-occurance matrix DFT features DWT Sketch DWT + Sketch DWT Sketch Texture DWT + Sketch + Texture DWT Sketch Texture DWT + Sketch + Texture DWT + Sketch + Texture Statistical features Genetic algorithm DCNN SVD Pre-trained CNN

BPNN

85.96

82.29

AdaBoost LDA

95.22 90.9 86.1 89.3 91.6 87.9 90.4 90.5 91.9 87.8 90.4 89.9 93.2 88.4 95.33 93.52 97.78 100

81.52 77.1 74 73.8 82.5 78.2 81.9 83.2 81.3 75.7 80.4 82.9 84.5 83.8 87.52 86.69 – 92.91

4.2. Selection of classifier The classifiers used in the comparison of results are linear SVM (SVM-LIN), kernel SVM (SVM-RBF), K-nearest neighbour (KNN), Naive Bayes, decision tree (Tree), LDA, and softmax. Among these classifiers, a classifier is selected depending on the performance in terms of accuracy on the pre-trained CNN features with G-filter as shown in Fig. 8. The linear SVM classifier performed well in classifying the different stages of cataract. The effect of over-fitting is very less for SVM classifier as compared to other classifiers. The evaluation of accuracies is also performed directly on input images without any G-filter to ensure the importance of pre-processing. These accuracies are plotted in Fig. 9.

SVM SVM SVM SVM BPNN BPNN BPNN BPNN Ensemble learning Decision tree SVM SVM SVM

obtained from each image to extract features from pre-trained CNN. The fc7 layer of pre-trained CNN is used to extract the features. The dimensions of features from fc7 layer is 4096. The proposed transfer learning for the classification of cataract yielded highest accuracy as compared to the existing methods [9–16] which is shown in Table 2. 4.4. Performance measures The performance results in terms of precision, recall and accuracy are presented in Fig. 10. From Fig. 10(a), it is clear that, the two class accuracy between normal and cataractous fundus is 100%. The remaining individual two class accuracies are measured and presented for reference in Fig. 10(b).

4.3. Comparisons

4.5. Importance of image quality

The collected dataset contains 800 images (200 normal, 200 mild, 200 moderate and 200 severe stage). Then G-channel is

The testing accuracy is evaluated with different training and testing datasets at different quality levels as shown in Table 3.

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Fig. 10. (a) Confusion matrix. (b) Possible two class accuracies among four classes.

Table 3 Testing accuracies with different image qualities.

transfer learning is pre-trained CNN which is trained with millions of non-medical images.

S. No

Training

Testing

Testing accuracy (%)

1 2 3 4

Good Good Poor Poor

Good Poor Good Poor

92 83.25 82.5 90

Table 4 Processing time details of proposed method. Process

Processing time (s)

Training (one fold) Testing (one fold) Load pre-trained CNN Reading of single image Feature extraction for single image Classification of single image

540 15 1.6432 0.0041 0.4585 0.0085

It is observed that, the classifier performs well when the quality level matches at training and testing phase. If quality level does not match at training and testing phase, then it leads to degradation in performance. It is difficult to decide then, if the wrong classification is due to poor image quality or because of poor performance of the classifier. So, an image quality selection module is incorporated in the proposed work to segregate good and poor quality images. 4.6. Processing time The execution time is evaluated for all the processes involved in the proposed classification method and is shown in Table 4.

6. Conclusions The pre-trained CNN for the transfer learning to carry out automatic cataract classification has been proposed. The transferred CNN has been used as a feature extraction technique and the extracted features have been applied to the SVM classifier. The use of CNN as a transfer learning is an efficient technique in the classification of cataract images. The experimental results on the overall dataset show the superiority of our method over existing methods. The results also conclude that the transfer learning (or fine tuning) is effective when training set is relatively small. Thus, transfer learning provides better accuracy in the case of cataract detection by fundus images. Automatic cataract detection methods via digital fundus images are able to detect the cataract without any ophthalmologist intervention. These types of non-invasive methods are helpful especially for the people in rural areas. Better feature extraction techniques are needed along with the classification algorithms. The use of CNN based feature extraction methods providing high accuracies in evaluating the cataract. The internet of things (IoT) based approaches along with automatic feature extraction techniques are beneficial. The patients can access the medical facilities from any place by using IoT. In the modern medical diagnosis, the combination of automatic disease detection methods and the IoT will further improve the medical facilities especially in rural areas. Conflict on interest None.

5. Discussion Acknowledgement The transfer learning based computer-aided diagnosis system for cataract detection has been proposed. In [9–16], DWT features have been utilized to classify the fundus cataract images. The accuracy obtained using hand-crafted feature extraction technique is 82.5% [12]. The DWT features are combined with sketchand texture-based features to increase the classification accuracy to 84.5% [12], which is the highest accuracy obtained by using handcrafted features followed by ensemble of classifiers. In [15], CNN has been exploited to evaluate the cataract and the method outperforms the previous hand-crafted feature extraction methods. The transfer learning used in the present work obtained 92.91% of classification accuracy which is better than the accuracy in [15]. In [15], the CNN is constructed with 5620 images. However, the transfer learning from AlexNet is more efficient method for classification and it is having less over-fitting issues as compared to CNN constructed from scratch. The reason for better efficiency in

This work was supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), India [Grant no. ECR/2017/000135]. The authors are thankful to all the ophthalmologists especially Dr. M. Manjulamma who spend their valuable time for labelling of the fundus retinal images used in this paper. The authors are thankful to the editor and anonymous reviewers for their constructive suggestions. We also express our gratitude towards Dr. Deepak Ranjan Nayak and Prof. Banshidhar Majhi for their useful comments in revision of the manuscript. References [1] D. Pascolini, S.P. Mariotti, Global estimates of visual impairment: 2010, Br. J. Ophthalmol. 96 (5) (2012) 614–618. [2] R.R. Bourne, S.R. Flaxman, T. Braithwaite, M.V. Cicinelli, A. Das, J.B. Jonas, J. Keeffe, J.H. Kempen, J. Leasher, H. Limburg, et al., Magnitude, temporal trends,

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