2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 7 , Issue 1 , PP: 30-40, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Nechirvan Asaad Zebari 1 * , Mehmet Emin Tenekeci 2
According to the American Society of Clinical Oncology, Computer-Aided Diagnosis (CAD) techniques have the tremendous possibility for the screening and early identification of melanoma. They are evaluated in terms of their current state-of-the-art, as well as current practices, challenges, and prospects in the areas of image screening, pre-processing of an image, segmentation of Region of Interest (ROI), feature extraction, feature selection, and classification of dermoscopic images. It is stated in this study that statistical information and outcomes from the most major implementations that have been reported to date are presented. We investigated the evaluation performance of many classifiers that had been developed specifically for the diagnosis of skin cancer. The fundamental aim of this paper is to develop a framework that will serve as a complete guideline for choosing relevant techniques for various elements of an automatic detection technique.
Computer-Aided Detection, Dermoscopic images, Skin cancer, Machine learning, Deep learning.
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