An improved bag of dense features for skin lesion recognition

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An improved bag of dense features for skin lesion recognition Pawan Kumar Upadhyay ⇑, Satish Chandra Department of Computer Science Engineering & Information Technology, Jaypee Institute of Information Technology, Noida, India

a r t i c l e

i n f o

Article history: Received 30 September 2018 Revised 4 February 2019 Accepted 17 February 2019 Available online xxxx Keywords: Gradient Location and Orientation Histogram (GLOH) Hybrid Image Descriptor (IHID) Bag of visual word (BoVW) Scale Invariant Feature Transform (SIFT) Support Vector Machine (SVM)

a b s t r a c t Skin is the largest and fastest growing organ in the human body. There are various types of skin lesions in which malignancy are non-invasively detected and recognized based on their local and global attributes of the image using an image-guided system. In this work, Gradient Location and Orientation Histogram and color features are fused together to construct the Inherently Hybrid Image Descriptor for skin lesion classification. The features obtained from these descriptor are combined to form a bag of visual words. The improved bag is used to categorize the skin lesion classes as malignant or benign using Support Vector Machine. The performance of the proposed method has been found considerably better than the current state-of-art. It also simplifies the process of diagnosis for undeclared abnormalities in the skin region. Ó 2019 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Skin lesion screening is a non-invasive imaging method which helps in the diagnosis of skin lesion without any surgical interventions. The skin lesions are primarily categorized into two major classes as malignant and benign. The detection and accurate classification of a lesion is the primary concern for skin lesion recognition system. The severity of lesion class depends on the perceptual features of the skin, commonly available in stratum corneum (on the surface layer) (Iyatomi et al., 2008; Celebi et al., 2007; Ballerini et al., 2013). The most severe class of pigmented lesion is melanoma and the maximum number of cases related to melanocytic lesion occurs in cold countries. In addition to this, nonmelanoma class of cancer is also increasing in around the world (Iyatomi et al., 2008; Ballerini et al., 2013). The various shades of color and repetitive elements of dermoscopic structure help to visualize the in-depth structures of a lesion in high-resolution images, which are quite difficult to perceive through the naked

⇑ Corresponding author. E-mail addresses: [email protected] (P.K. Upadhyay), satish.chandra@ jiit.ac.in (S. Chandra). Peer review under responsibility of King Saud University.

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eye during examination by the medical expert (Wettschereck et al., 1997; Armengol, 2011). In addition to this, an improvement in each phase of Computer-Aided Diagnostic System (CAD), helps to avoid a histological analysis of skin lesions thereby reducing the rate of biopsies (Cliff, 2014; Rosendahl et al., 2012; Mikolajczyk et al., 2010). In this paper, we give an overview of the recent development of feature-based skin lesion recognition system. The contribution of this work is to devise a set of dermoscopic key features for recognition of skin lesion samples into malignant or benign. This paper is organized as follows. Section 2 presents an overview of existing methods used for different classes of skin lesion detection and classification. Some improved strategies, as well as extensions as the feature vector, are also discussed in this section. Section 3 investigates common dermoscopic structures in each group of lesion. It helps to validate the reason of merging of skin lesion classes into two different categories as malignant or benign. Section 4 describes proposed method of feature bag fusion which is used further for skin lesion classification. Finally, Section 5 describes the experimental results and its analysis which help to justify the proposed method. 2. Related works A medical image usually shows complex feature space distributions because of the two precise reasons as intra-class variability and inter-class ambiguity. The diagnosis of pigmented skin lesions using dermoscopy follows certain standard rules. The fundamental

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Please cite this article as: P. K. Upadhyay and S. Chandra, An improved bag of dense features for skin lesion recognition, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.007

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rule of melanoma detection is ABCD (Asymmetry, Border, Color, and Diameter) which helps to develop the structural differences between the classes of skin melanoma with certain color variegation (Wadhawan et al., 2011; Mikos et al., 2012). Other than that, medical experts and researchers develop some other standards methods (e.g. CASH (Bay et al., 2008) which employs the 4points and 7-points checklist, is used to classify a skin melanoma as malignant or benign. Although, there are standard methods for melanoma cancer but other skin cancers recognition are still an unreached area for CAD system. Based on our knowledge, there are only two proposals in which melanocytic and nonmelanocytic skin lesion classification performed. These can be handled nicely by feature descriptor based methods for skin lesion classification (Iyatomi et al., 2008; Celebi et al., 2007; Ballerini et al., 2013; Barata et al., 2013). These aspects can be handled nicely either by providing the complete (per pixel) information of image. The spatial information of a pixel in image space is described by the descriptors. There are variety of descriptors which represents global and local features of image space. The global features vector correspond to complete image and local features are from sub region of image space. The descriptors which are used to describe the melanoma elements (dermoscopic structures) and it pigmentation at each point of image space are formed by the combination of color descriptor and SIFT descriptor and form a Color-SIFT fused descriptor (Moreno et al., 2009; Salahat and Qasaimeh, 2017; Ahnlide et al., 2016). In this descriptor, SIFT used to mark the number of edge of lesion and color used to mark the pigmented substances of melanoma (Moreels and Perona, 2007; Salahat and Qasaimeh, 2017). If the numbers of elements (structure or color) are more than one then, it is considered to be chaos of structures or colors. It can be verified by related clues for each of the chaos (Epstein, 1985; Mikolajczyk et al., 2005). Before feature extraction, various steps related to dermoscopic image processing as image enhancement and segmentation have been accounted by several authors. Enhancing the image includes color calibration with certain normalization (Iyatomi et al., 2011; Schaefer et al., 2011; Rigel et al., 2010). In addition to this, segmentation techniques comprises of manual, semi-automated and automated methods used for the lesion class and its associated patterns. Once, the segmentation is performed on image, its related key features are extracted from it which represents the color and structure of dermoscopic images. Each Image descriptor helps to transform a single value feature to feature vector in given image space and helps to improve its classification accuracy. There are a large variety of textural descriptors which includes: Gabor filter, HAAR wavelet, Gray Level Co-Occurrence Matrix (GLCM) and shape descriptors are used in active shape model (ASM) for any active instance (Amira et al., 2014; Salahat and Qasaimeh, 2017). The most frequently used color of descriptors are color moments, color histograms (Barata et al., 2014; Situ et al., 2008; Sivic and Zisserman, 2003). Along with color, textural descriptors is also important as speedup robust descriptor (SURF), is used to discriminate different type of melanoma patterns (Catarina et al., 2013; Amira et al., 2014; Ramiro and Bykbaev, 2012). The present findings seem to be consistent with other research which follows the same principal of image analysis are performed in three major steps: (i) the borders identification and detection (Fix and Hodges, 1989) (ii) image features are extracted from region of interest (Bay et al., 2008; Celebi, 2009; Barata et al., 2015) (iii) and, evaluate these features with pre-calculated features of each class of skin lesion and perform skin lesion classification using classification method. In order to obtained better classification accuracy, we are targeting for more dense bag of feature

vectors using BoVW techniques. The dense feature bag consists of two of feature descriptor having maximum level of correlation. This can be achieved by local descriptor which extraction the pixel level information from an image as two of its constituents as multilevel pigmentation and structural features from the key points (edges, corner andregion) and generates dermoscopic patterns (Moreno et al., 2009; Moreels and Perona, 2007). 3. Structured classes of skin lesion The standard dataset of high resolution color images are captured from high definition camera. To best of our knowledge, they are gold standard image library (Ballerini et al., 2013) of dermofit. There are various classes of skin lesion which are group into benign or malignant are based on lesion attributes as illustrated in the Table 1, with the certain attributes of lesion which follows the laws of nature and benign seems to be symmetrical but malignancy supports asymmetrical in terms of pattern and color. The related images of class sample which are grouped into malignant or benign are shown below: Group structure of skin lesion: The standard image library samples are gold standard i.e. each sample of it, is approved by dermopathologist as well as dermatologist. There are no certain clues for premalignant or nevus classes which can verify them to be completely malignant or benign. In this paper, Out of the ten classes of standard library, we have selected only six classes of skin lesions which are kept into any one of the group as shown in Fig. 1. This is because the remaining classes (Actinic keratosis (AK), Seborrheic keratosis (SK) and Intraepithelial Carcinoma (IEC)) requires degree of malignancy which is not the exact scale of recognition as malignant or benign. In addition to this, The class ME (nevus) can never be malignant and considered to be benign when the sample is suspicious and other classes such as AK, SK, IEC are all premalignant always. The group selection of samples are based on certain attributes which are described below in Fig. 1. 4. Detection of dermoscopic features In this section, the authors describe the procedure of extraction for low level key descriptors which are used to identify the clues for chaotic dermoscopic patterns of skin lesion with various shades of color (Epstein, 1985; Mikolajczyk et al., 2005). The low level features which are obtained from GLOH and HSV color descriptor are used to recognize malignant or benign behavior of skin lesion. 4.1. Dermoscopic structural features using GLOH descriptor The computations for the GLOH log polar histogram are done by Eqs. (1) and (2). It computes two of the component at point (x, z) of an image, as their magnitude is the hypotenuse in Eq. (1) and the angle is the arctangent in Eq. (2) from the Cartesian coordinate of standard HOG are given below:

pðx; yÞ ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðIðx þ 1; zÞ  Iðx  1; zÞÞ2 þ ðIðx; z þ 1Þ  Iðx; z  1ÞÞ2

ð1Þ

Table 1 Attributes of skin lesion as benign or malignant. Lesion Attributes

Benign

Malignant

Dermoscopic Structures Dermoscopic colors

Symmetrical in patterns Symmetrical in color

Asymmetrical in patterns Asymmetrical in color

Please cite this article as: P. K. Upadhyay and S. Chandra, An improved bag of dense features for skin lesion recognition, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.007

P.K. Upadhyay, S. Chandra / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

Group/Skin lesion Class

A:Sample Class1

B: Sample Class2

3

C:Sample Class 3

Benign

Malignant

Fig. 1. Group samples of skin lesions as Benign: (A) Pyogenic Granuloma (PYO), (B) Haemangioma (HAEM), (C) Dermatofibroma (DF) Malignant: (A) Basal Cell Carcinoma (BCC), (B) Squamous Cell Carcinoma (SCC), (C) Malignant Melanoma (ML).

hðx; yÞ ¼ tan1 fðIðx; z þ 1Þ  Iðx; z  1ÞÞ=ðIðx þ 1; zÞ  Iðx  1; zÞÞg ð2Þ Log polar format is added to the SIFT descriptor which helps to enhance the accuracy of descriptor and amplify the rotational invariance (Ramiro and Bykbaev, 2012; Zhou et al., 2009). GLOH behave similar to the SIFT-HOG having histogram values of gradient magnitude and direction. GLOH are essentially represented as 3D histogram. It is represented as 128D (8  4  4) in Cartesian co-ordinates and converted to polar coordinate system by binning the components as a gradient magnitude and its direction. It helps to space the skin lesion region into 17 bins with 8 way direction of gradient. The radically distributed bins are able to sum the gradient information in polar coordinates and capitulates 272 bin histogram. GLOH descriptor generate considerably important features for gradient information which is ignored in SIFT descriptor. Another key feature for skin lesion recognition is color and it is computed with the help of HSV color descriptor are discuss in next subsection. 4.2. Pigmented skin lesion extraction using color features Color descriptor extract set of color features which describe the color properties inside the lesion. A general approach is to selected color space that have a relation with the human perception of vision or that are biologically inspired. The selected space characterize the colors model in a way similar to that of the human mind, namely each color is characterized by a Hue, Saturation, and Value. The HSV color model are perceptually uniform and device dependent and help to process the dermoscopy images more keenly. Most popular method for skin lesion detection and recognition is one dimension color descriptor which give 1D histogram. In HSV color descriptor, the complete color space is uniformly quantized into HSV color space with the dimension of 192, and quantization represents each channel as 12 hues (H), 4 saturations (S) and 4 intensities (V) respectively. The quantized space encapsulate as histogram bins having 192 dimension vector represent color features from the image grid patch of size (4  4). It depicts that Hue (change in color) parameter is varying with orientation and other two parameters as Saturation (color depth) and Value (central axis) are moving in the similar direction.

5. Proposed method The proposed method consists of three phases as describe above in Fig. 2: (1) formation of IHID, used to describe the dermoscopic attributes of lesions (2) formation of BoVW (3) SVM classifier used to classify the word index of lesion into two categories. (1) IHID formation: GLOH features are extracted from the patches of gray scale version of color image and unable to preserve the chromatic saliency (distinctiveness in chromatic regions) of skin lesion. For converging the chrominance attribute of image pixels in isoluminance space, it is required to map the RGB color space model to HSV color space. The obtained color features explicitly represent the color saliency along with the structural feature descriptor. HSV color component are computed at the same points where GLOH features has been detected. The new image descriptor is formed by the fusion of two features vector extracted from the each pixel of the image patch by following the algorithm 1.

Fig. 2. Proposed Method.

Please cite this article as: P. K. Upadhyay and S. Chandra, An improved bag of dense features for skin lesion recognition, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.007

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P.K. Upadhyay, S. Chandra / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

where k represents to number of clusters or visual words.

Algorithm 1: Proposed descriptor

3. Finally, the mapping of every descriptor to the nearest visual word is based on the following equation given below

1. Input: Image Im 2. VDHSV(Im) <- u 3. RGLOH <- Detected GLOH Features from Key-points(Im) 4. for single feature point r in RGLOH do 5. VDHSV(r) <- u 6. VD(r) <- extract image patch(r) 7. for each s in VD(r) do 8. VDGLOH(s) <- extract GLOH features (s) 9. VDHSV(s) <- extract HSV color feature(s) 10. VDIHID(s) <- (VDGLOH(s) uVDHSV(s)) 11. end for 12. VDIHID = VDIHID(s) 13. output: VDIHID

VWðVDIHIDn Þ ¼ argminDistVW2VV ðVW; VDIHID n Þ

The proposed descriptor (VD IHID) extracts a set of fused features locally from image patches and these are considered to be key feature vectors for skin lesions, as shown in the Fig. 3. These features are obtained from dissimilar patches of skin lesion images and it signifies that the region of interest as lesions possess different shades of color and dermoscopic structures or not. This kind of information would be watered down or even missed, if a global representation of features was considered. (2) Representation of IHID as BoVW Fused descriptor for skin lesion is modeled into BoVW and it describes the depicted contents of skin lesion of six different classes. The steps for BoVW are as follows: 1. In BoVW technique, an input image (I) is represented as a set of image descriptors as describe in Equation (3)

I ¼fVD IHID1; VD IHID2; VD IHID3;    VD IHID i g

ð3Þ

where i denote the total number of image descriptors 2. To reduce the dimensions of IHID vectors, an unsupervised clustering technique known as k-means is imply on the extracted set of features to locate the cluster centers that form a visual vocabulary (VV) are describe as follows:

  VV ¼ VW1; VW2; VW3 ; VW4; VW5 ; VW6;    ::VWk

ð4Þ

ð5Þ

Here, VW(VD IHID n) represents the visual word assigned to nth descriptor and the distance argminDist (VW, VD IHID n), signifies the distance between the descriptor VDIHID n and visual word VW. The clustering is required to reduce the dimensionality of feature vector and represent the feature space in compact form. The final representation of BoVW model is in a form of histogram which gives the distribution of visual words. The count of bins in histogram is equal to number of visual words in a dictionary (i.e. k). The size of dictionary for individual or fused bag of features in the current system is considered to be 192 (12  4  4), which is based on the length of HSV color descriptor. (3) Skin lesion classifier: The number of rows represents the total number of image samples. The selection of image samples is based on the class having minimum number of sample i.e. 24 and total number of sample are 144 samples out-of 826 from six different classes. There are ten different classes in standard image library out-of them only six classes of lesion are considered for input because remaining classes belongs to pre-malignant. These premalignant classes requires degree of malignancy requires more clinical measures to computer, so we remove from the input set of data samples. Then the considered samples (144) are partitioned into training (103) and testing (43) randomly. The obtained bag of features from six different class samples of skin lesion is classified into malignant or benign using SVM. SVM is a discriminative classifier that learns a decision boundary that maximizes the margin between the classes. It is able to manage the visual polysemy of key features for skin lesion recognition. RBF kernel with chi -squared distance between the histograms is able to discriminate the skin lesion classes as malignant or benign more accurately. SVM is a linear classifier, for nonlinearity, it is to be transformed to other space using nonlinear kernel. v2 RBF kernel of SVM is computationally fast and accurate for BoVW method.

Fig. 3. Skin lesion diagnosis using proposed method.

Please cite this article as: P. K. Upadhyay and S. Chandra, An improved bag of dense features for skin lesion recognition, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.007

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Recognition of lesions by SVM were done in following steps: a) The size of the training set is considered to be 103  192. It represents that each image samples has fixed size of visual vocabulary, which is represented as histogram bins. b) The histograms bins are regularizes with L1 optimisers, in order to account the equal set of features in fused bag and helps to remove redundant feature space. c) More complex kernel functions as Radial basis function (RBF) have been used to model non-linear decision boundaries are as follows:

KdðRBFÞðx;yÞ ¼ e1=cdv2ðx;yÞ

Table 3 Confusion matrix: GLOH descriptor. Group/skin lesion

Malignant

Benign

Malignant Benign

0.36 0.33

0.64 0.67

Table 4 Confusion matrix: HSV color descriptor. Group/skin lesion

Malignant

Benign

Malignant Benign

0.70 0.17

0.30 0.83

ð6Þ

where dv2(x; y) can be chosen to manage the intra class variegation in feature space of distinct samples of skin lesion. The v2 distance consider to be better distance metrics when comparing the histogram structures of skin lesions in BoVW method.

Table 5 Confusion matrix: proposed descriptor. Group/skin lesion

Malignant

Benign

Malignant Benign

0.72 0.17

0.28 0.83

6. Results and discussions A generalized BoVW method is used for skin lesion diagnosis having fixed length of vocabulary for each image samples using single or fused descriptors. The experiments are performed on the platform of MATLAB-2014b with system configuration of Dual Core I-5 Processor having 8 GB RAM and GPU of 920 M series with the size of 2 GB. Parametric evaluation for dense Bag-of-features: As mentioned above the dense features are able to detect the symmetry of dermoscopic key feature as dermoscopic structures and colors and able to recognize the malignant or benign samples of skin lesions. Following performance measures are considered for the lesion classification: (a) No of clusters, (b) No of features (c) No of iterations or time to converge for iteration to generate efficient visual bag of hybrid features. The basic attributes for the computation of descriptor are (a) Strong features are selected from each of the bag and keeping the size of the vocabulary is fixed i.e.192. (b) The key point selection is based on constant radius for each of the descriptor. (c) So, the feature vector which are encode in a proposed bag of visual word is 144  192. Results of the proposed method is describes below in the following Tables 3–5 as given below: Performances of Individual Bag of Feature: In this sub-section, Tables 2–4 shows the confusion matrix of individual and fused features which are obtained by evaluating these features on SVM classifier and compute averaged run time after splitting the data in the ratio of [T(70):T(30)]. The values along the diagonal of the matrixes represent the recognition rate of samples corresponds to each class, and the numbers other then diagonal show the error rate (misclassification rate) denoted as error rate (ER = 17). We can see that color feature perform well for classes with very distinguishable color such as benign (RR = 83%) and malignant (RR = 70%) in Table 4. However, the GLOH features are not able to distinguish well between benign (RR = 67%) and malignant (36%) as shown in the Table 3.

Performances of fused bag of features: Table 5 shows the automated recognition rate (ARR) for finest combinations among the two features. Best result 78% accuracy is achieved by combining GLOH and HSV Color feature. This validates the usefulness of the color feature descriptor for skin lesion recognition. Table 5 shows the confusion matrix of fused features which leads to a enhancement in skin lesion classification. The results are shown in Table 3 for GLOH descriptor which detect malignancy with the recognition rate of (RR = 36%). Furthermore, the results are improved when the classification was performed on fused features (after fusion with color feature) as shown in Table 5 with the recognition rate of malignant (RR = 72%) and benign (RR = 83%). The weighted values are compared in confusion matrix and it shows that the color feature vector is dominating the key feature vector obtained from the bag of IHID descriptor. Benign/Malignant is considered to be a false negative rate; our objective is to improve the true positive rate in the confusion matrix as describe in Table 5 of bag of proposed descriptor. The obtained features in bag of HSV color descriptor and bag of proposed descriptor were same for fixed vocabulary (192) as shown in Table 2, is the main reason of constant false negative rate. For dermoscopic skin lesion recognition, the mean accuracy of SVM classifier for GLOH descriptor (52%),HSV Color descriptor (76%) and IHID descriptor (78%) is describe above in the Fig. 4. In addition to this, they obtained sensitivity, specificity and accuracy

Table 2 Comparison of descriptors with fixed size of vocabulary. Descriptors used to describes skin lesions

No of clusters (K)/size of Vocabulary

No of features

No of iterations/ time to converge

GLOH HSV Color IHID

192 192 192

2022 181,656 181,656

7/0.94 s/iteration 48/0.94 s/iterations 51/0.85 s/iterations

Fig. 4. Comparison of proposed descriptor with conventional descriptor using SVM.

Please cite this article as: P. K. Upadhyay and S. Chandra, An improved bag of dense features for skin lesion recognition, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.007

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Table 6 Comparison of proposed method with the current State-of -the-art.

References

Authors

Method

No of classes

Accuracy (%)

Ballerini et al. (2013) Situ et al. (2008)

Color and texture, K-NN

5

74

Color histogram, Gabor filter, BoVW, K-NN GLCM, PNN Neural network Color histogram, Haar wavelet, SVM GLOH, HSVcolor, BoVW, SVM

2

82.2

2 2

69.5 76.4

6

78

Mikos et al. (2012) Wadhawan et al. (2011) Proposed method

for two class of lesion are also describes in the Fig. 4. The accurate detection of skin lesion class is crucial for successful treatment. However, there are various methods for skin lesions recognition but the proposed method has the potential of enhancing the current clinical paradigm in the domain of dermatology. Comparison with a Current-state-of-art: We compared our work with lesion recognition methods proposed by several researchers in the domain of skin lesion classification and recognition. A direct comparison is not possible because of different dataset and validation measures used in these methods. The results are shown in Table 6. In the work, we identifies the simplified criteria of skin lesion diagnostic and help to boost its recognition rate (RR) up to 78%. The proposed method uses bag of visual words (BoVW) technique which help to identify the clues for chaotic skin lesion using dermoscopic structures and colors features and discriminate them, to be malignant or benign. In the table (Situ et al., 2008), a comparison is drawn between similar technique having identical procedure of skin lesion recognition. These procedures were based on feature analysis of dermoscopic image used to describe the patterns of skin lesion or its colors. In fact, the complexity of this algorithm is O(V*D*P) where V is the vocabulary size (number of visual words), D is the dimension of the feature and P is the number of detected points.

7. Conclusions This work has shown that GLOH and color features were fused together to construct the new descriptor IHID for skin lesion recognition. The features obtained from these descriptor are represented in a form of bag of visual words, which was used to categorize the skin lesion classes as malignant or benign using Support Vector Machine. The proposed approach performed screening on six different class samples of skin lesion, without any incident of projection, scale space and any lighting condition. Furthermore, it is fast, accurate and cost-effective approach which leads to a medical description of lesion in simplest way with the average accuracy of 78%. The results obtained in the proposed approach are quite promising as compared to current state-of-art. In future, the proposed approach may be applied to different modalities of medical images related to many other vital organs. Furthermore, some modifications will be required in the proposed method so that degree of malignancy may be considered and it will helps to recognize the premalignant class samples of skin lesion.

Conflict of interest The authors declare that they have no competing interests.

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Please cite this article as: P. K. Upadhyay and S. Chandra, An improved bag of dense features for skin lesion recognition, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.02.007