Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)

Document Type: Research Articles

Authors

1 Research Scholar, Department of Computer Science, Periyar University, Tamil Nadu, India.

2 Principal, Siri PSG Arts and Science College for Women, Sankagiri, 637301, Salem, Tamil Nadu, India.

Abstract

Objective: The main objective of this study is to improve the classification performance of melanoma using deep
learning based automatic skin lesion segmentation. It can be assist medical experts on early diagnosis of melanoma
on dermoscopy images. Methods: First A Convolutional Neural Network (CNN) based U-net algorithm is used for
segmentation process. Then extract color, texture and shape features from the segmented image using Local Binary
Pattern ( LBP), Edge Histogram (EH), Histogram of Oriented Gradients (HOG) and Gabor method. Finally all the
features extracted from these methods were fed into the Support Vector Machine (SVM), Random Forest (RF), K-Nearest
Neighbor (KNN) and Naïve Bayes (NB) classifiers to diagnose the skin image which is either melanoma or benign
lesions. Results: Experimental results show the effectiveness of the proposed method. The Dice co-efficiency value
of 77.5% is achieved for image segmentation and SVM classifier produced 85.19% of accuracy. Conclusion: In deep
learning environment, U-Net segmentation algorithm is found to be the best method for segmentation and it helps to
improve the classification performance.

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