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Skin Lesion Classification System Using Shearlets

S. Mohan Kumar*, T. Kumanan

Department of Computer Science and Engineering, Meenakshi Academy of Higher Education and Research, Chennai, 600078, Tamil Nadu, India

* Corresponding Author: S. Mohan Kumar. Email: email

Computer Systems Science and Engineering 2023, 44(1), 833-844.


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 PH2 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 3rd level with 8-directions.


Cite This Article

APA Style
Kumar, S.M., Kumanan, T. (2023). Skin lesion classification system using shearlets. Computer Systems Science and Engineering, 44(1), 833-844.
Vancouver Style
Kumar SM, Kumanan T. Skin lesion classification system using shearlets. Comput Syst Sci Eng. 2023;44(1):833-844
IEEE Style
S.M. Kumar and T. Kumanan, "Skin Lesion Classification System Using Shearlets," Comput. Syst. Sci. Eng., vol. 44, no. 1, pp. 833-844. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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