Open Access

ARTICLE

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:

Computer Systems Science and Engineering 2023, 44(1), 833-844. https://doi.org/10.32604/csse.2023.022385

Received 05 August 2021; Accepted 09 October 2021; Issue published 01 June 2022

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 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.

Keywords

Skin lesion classification; non-subsampled shearlet transform; sub-band coefficients; energy feature; support vector machine

Cite This Article

S. Mohan Kumar and T. Kumanan, "Skin lesion classification system using shearlets," Computer Systems Science and Engineering, vol. 44, no.1, pp. 833–844, 2023.



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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