Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images 1Kavimathi.P 2Sivagnanasubramanian.S.P 1,2

1,2

Department of Electronics and Communication Engineering Sri Venkateswara College Of Engineering, Pennalur, Sriperumbudur 602117 India Abstract

Skin cancer is one of the major causes of deaths in recent days. Early detection of skin cancer reduces death at higher rate.

Ceroscopy is one of the major modalities used in diagnosis of skin lesions. Skin lesions are of different types. Among them the most common types of skin lesion found in human are melanoma, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC).The accurate diagnosis information cannot be obtained by human interpretation. In order to overcome the error due to human interpretation an efficient computerized image analysis system has been developed. The proposed image analysis system consists of preprocessing, lesion segmentation, feature extraction and classification. In classification, different types of classifiers such as support vector machine (SVM), probabilistic neural network (PNN) and adaptive neurofuzzy inference system (ANFIS) are applied to classify the skin cancer types and their performance is compared using the evaluated parameters.

Keyword- Skin cancer, Feature extraction, Adaptive Neurofuzzy inference system, Thresholding __________________________________________________________________________________________________

I. INTRODUCTION In recent years skin cancer is identified as one of the major causes of death. The skin lesions are of different types but the most common types of skin lesions are basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and melanoma. Basal cell carcinoma is the most common form of skin cancer. It appears as a small raised bump which has a pearly appearance. It occurs in areas of skin which received more exposure to sunlight. Squamous cell carcinoma occurs as a red bump that does not heal.

Melanoma is the deadliest cancer which grows rapidly with different colors and abnormal shapes. Among these three types, melanoma is the deadliest form of skin cancer. The deadliest form of skin cancer. The death due to melanoma is increasing at an alarming rate of 3% per year. The death rate due to skin cancer can be reduced at higher rate by early detection. One of the major tool to detect skin lesion is dermoscopy. Dermoscopy refers to the examination of skin using skin surface microscopy. Dermoscopy is mainly used in the diagnosis of pigmented skin lesions. The colours found in pigmented skin lesions are black, brown, red, blue, grey, yellow and white. Using dermo copy, the lesion pigmentation is evaluated in terms of colour and structure. The pigmented skin lesions are of different types. Diagnosis helps in easy and efficient detection of melanoma.

II. RELATED WORKS Zhou et al. implemented automatic hair detection using curvilinear analysis. By using feature guided exemplar-based inpainting the artifact pixels were replaced. This algorithm can be applied only to dark hair.

Karargyris et al.implemented advanced image processing mobile application for monitoring skin cancer.An inexpensive accessory was used to improve the quality of images. But their image database was too small containing 6 images of benign and 6 images of suspicious.

Sookpotharom Support conducted a technical survey to identify the best performing components involved in the BOF model and design. Image border detection is an important step to help the physicians to identify the skin lesions in thermoscopic images.

Mahmoud proposed an automatic skin cancer classification system. The proposed system includes preprocessing to enhance the image. Two segmentation methods used to segment the skin lesion. The features used for classification is the coefficients created by Wavelet decompositions and simple wrapper curvelet.

Black ledge presented an overview of a new web-based technology for screening of skin cancer. The technology is based on an expert system designed to classify moles through an analysis of a good quality digital image uploaded by the user of the system. The technology is an example of an intensive application and service in the area of Health Informatics and has been developed as a personalized e-Health Service.

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Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images (GRDJE / CONFERENCE / ICIET - 2016 / 072)

III. METHODOLOGY The methodology block is given in below fig.1 which describes the flow of work to be followed in the diagnosis of skin cancer.

Fig. 1: Flow chart of proposed image analysis system

Initially the input image is taken for preprocessing .The preprocessing is the process of removal of noise and enhancing the image for furtherprocess. The dermoscopy images may have noise in the form of hair, bubbles,etc. In order to remove those noises and enhance the image the preprocessing step is used.After preprocessing, the segmentation is used to segment the lesion from background. The feature is extracted from the segmented lesion and its given as input to the classifier.Finally the classifier classifies the image as BCC or SCC or melanoma.

A. Preprocessing Image preprocessing is an important step in the diagnosis of skin lesions. Because preprocessing is followed by segmentation.

Segmentation is the crucial step itself that will affect the further processes including the final diagnosis.The dermoscopy image may consist of some artifacts such as hairs, bubbles,etc. The presence of hair in the image may disturb the identification of skin lesions. For these reasons, preprocessing is considered as the important step. In the proposed system the morphological closing operation is applied to the dermoscopic images to exclude the hair. After excluding the hair, the missing hair gap position is filled using bicubic interpolation. The wiener filter is applied to the hair excluded image to smoothen the noise and finally the image is enhanced using histogram equalization followed by morphological closing operation.

Fig. 2: Original image with hair

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Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images (GRDJE / CONFERENCE / ICIET - 2016 / 072)

Fig. 3: Acquired image after excluding hair

B. Lesion Segmentation Segmentation is the process of segmenting the skin lesion from the background. Otsu’s segmentation is the method used in segmenting the pigmented lesion from the background in the proposed system. Otsu’s method is the thresholding method which is fully unsupervised. After applying Otsu’s segmentation the edges in the segmented skin lesion is smoothen by morphological closing and opening operation.

Fig .4: Segmented Image

C. Feature Extraction Feature extraction is the important tool used in analyzing and exploring the images. Each skin lesion has its unique feature. With the help of extracted features the classification can be performed efficiently because classification completely depends on the extracted features.The extracted features are mean, standard deviation and shape features. The mean and standard deviation provides information about the pigmentation of skin between the lesion and surrounding skin.

D. Classification Classification is the data analysis method used to predict the categorical data.Classification incorporates two process such as training and the testing .In training phase the pre-determined data and its associated class labels are used for classification.Fig.5 shows the testing and training phases of classification.

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Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images (GRDJE / CONFERENCE / ICIET - 2016 / 072)

Fig. 5: Training and Testing phase

In this paper three types of classifiers such as SVM, PNN and ANFIS are used to classify the given input dermoscopic image as BCC or SCC or Melanoma.

E. Using Support Vector Machine Support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. SVM can efficiently perform non-linear classification similar to linear classification with the help of kernel trick. The clustering algorithm that provides improvement to the support vector machine is known as support vector clustering. Support vector machine uses hyper plane.

F. Using Probabilistic Neural Network Probabilistic neural network is closely related to parzen window pdf estimator. PNN consist of four layers. Architecture of PNN is shown in fig .6.The input nodes are the set of measurements. The second layer consists of Gaussian functions formed using the given set of data points as centers. Averaging operation of the outputs from second layer is performed by the third layer. Finally the fourth layer performs a vote, selecting the largest value.

Fig. 6: PNN Architecture

G. Using Adaptive Neuro fuzzy Inference System The ANFIS network consists of two parts. The first part is antecedent part and the second is the conclusion part.These two parts are connected to each other in network form by rules.Fig.7 shows the structure of ANFIS.It consist of five layers. The first layer performs fuzzification process, second layer performs the fuzzy AND of the antecedent part of the fuzzy rules. Normalizations of membership functions (MFs) are performed by third layer. The fourth layer executes the fuzzy rules and the last layer computes the output of fuzzy system by summing up the outputs of fourth layer.

The feed forward equation of ANFIS is given as:

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Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images (GRDJE / CONFERENCE / ICIET - 2016 / 072)

Fig. 7: ANFIS Architecture

IV. EXPERIMENTAL RESULTS In the proposed system three types of classifiers such as SVM,PNN and ANFIS are tested on a dataset containing 200 images incorporating three types of skin lesions such as BCC,SCC and melanoma. The performance of these classifiers is determined based on the evaluated parameters. The parameters such as sensitivity,specificity and accuracy are calculated for each classifier.

These parameters can be evaluated using the formula given below: Sensitivity=TP/(TP+FN)(4) Specificity=TN/(TN+FP)(5) Accuracy=(TP+TN)/(TP+TN+FP+FN)(6) CLASSIFIERS

SENSITIVITY (%)

SPECIFICITY (%)

ACCURACY (%)

72

80

76

80

80

80

SVM PNN ANFIS 90 85 Table 1: Evaluated parameters

88

Fig. 8: Performance comparison of three classifiers

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Analysing the Performance of Classifiers for the Detection of Skin Cancer with Dermoscopic Images (GRDJE / CONFERENCE / ICIET - 2016 / 072)

V. CONCLUSION An image analysis system has been designed with efficient algorithms to detect the dermoscopy images. The diagnosing methodology uses digital image processing techniques and three different types of classifiers are applied to the dataset containing 200 images that includes three types of skin lesions.The performances of these classifiers are measured using the parameters such as sensitivity,specificity and accuracy.The experimental results shows that ANFIS performs better compared to SVM and PNN classifier.

REFERENCES [1] S.G.Mallat,“A Theory for Multiresolution, (1989) Signal Decomposition: The Wavelet Representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 7, no. 11, pp. 674–93.

[2] P.Bilek,A. B. Cognetta,M. Landthaler, T. Merckle,F. Nachbar,G. Plewig, W. Stolz and T. Vogt(1994)“The ABCD ruleof dermatoscopy:High prospective value in the diagnosis of doubtful melanocytic skin lesions,” J. Amer. Acad. Dermatol., vol.

30, pp.551–559.

[3] R. Mahmud,A. Ramli, M.Al-Qdaha, (2005) “A system of Micro-Calcifications Detection and Evaluation of the Radiologist: Comparative Study of the three main races in Malaysia”, Elsevier Journal of Computers in Biology and Medicine vol. 35, no.

10.

[4] F.Godtliebsen,H. M. Kirchesch,K. Møllersen, T. G. Schopf, and ,(2010)``Unsupervised segmentation for digital dermoscopic images,'' Skin Res.Technol., vol. 16, no. 4, pp. 401_407.

[5] M. S. Atkins, T. K. Lee, N. H. Nguyen,(2010) and ``Segmentation of light and dark hair in dermoscopic images: A hybrid approach using a universal kernel,'' Proc. SPIE, vol. 7623, p. 76234N. [6] Q. Abbas, I. Fondon, and M. Rashid,(2011) ``Unsupervised skin lesions border detection via two-dimensional image analysis,''Comput. Methods Programs Biomed., vol. 104, no. 3, pp. e1_e15.

[7] Dimitri Dubovitski, Jonathan Blackledge,(2011) “Mole test: A Web-based Skin Cancer Screening System”, Intensive 2011: The Third International Conference on Resource Intensive Applications and Services, vol: 978-1-61208-006-2, pp. 22 – 29.

[8] M.d.KhaladAbuMahmoud,Wighton.P,(2011)“The Automatic Identification of Melanoma by Wavelet and Curve let Analysis: Study Based on Neural Network Classification”, 11th IEEE International Conference on Hybrid Intelligent Systems (HIS), pp: 680- 685.

[9] A.Karargyris,O.Karargyris,andDERMA/Care,(2012)Advanced image-processing mobile application for monitoring skincancer,'' in Proc. IEEE 24th Int. Conf. Tools Artif. Intell. (ICTAI), pp. 1_7.

[10] Nilkamal S. Ramteke, Shweta V. Jain,(2013) “Analysis of Skin Cancer Using Fuzzy and Wavelet Technique –Review & Proposed New Algorithm” International Journal of Engineering Trends and Technology(IJETT) ,Volume 4, Issue 6 [11] Sookpotharom Supot,(2014) “Skin Lesion Detection of Dermoscopy Images Using Estimate Localization Technique”, pg. no.

833-836.

[12] http://www.healthline.com/health/skin-lesions [13] http://en.wikipedia.org/wiki/Skin_cancer

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