Skin Lesion Classification System Using Shearlets  

作　　者：S.Mohan Kumar T.Kumanan 

机构地区：[1]Department of Computer Science and Engineering,Meenakshi Academy of Higher Education and Research,Chennai,600078,Tamil Nadu,India

出　　处：《Computer Systems Science & Engineering》2023年第1期833-844,共12页计算机系统科学与工程（英文）

摘　　要：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.Atfirst,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 fea-tures 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 fea-tures obtained from 3^(rd) level with 8-directions.

关 键 词：Skin lesion classification non-subsampled shearlet transform sub-band coefficients energy feature support vector machine 

分 类 号：R739.5[医药卫生—肿瘤]