Skin Lesion Classification System Using Shearlets

Abstract

The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST's sub-bands, energy features are extracted and stored in the feature database for training. SVM classifier is used for the classification of skin lesion images. The dermoscopic skin images are obtained from PH 2 database which comprises of 200 dermoscopic color images with melanocytic lesions. The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic (ROC) curves. The SLC system achieves 96% classification accuracy using NSST's energy features obtained from 3 rd level with 8-directions.

An Identification Method of Feature Interpretation for Melanoma Using Machine Learning

“…To evaluate the generalization capability of SVM models, the application of crossvalidation techniques is a common practice [41,42]. Prior studies have effectively demonstrated the generalization achieved through cross-validation [43] by training SVM models on a discovery dataset and subsequently assessing their performance using a distinct replicated dataset [44]. In the approach, internal tenfold cross-validation [45] was employed to optimize model performance.…”