Recent innovations in machine learning for skin cancer lesion analysis and classification: A comprehensive analysis of computer-aided diagnosis

Abstract

The global primary health concern of skin cancer emphasizes the need for quick and accurate diagnosis to improve patient outcomes. Although, it might be challenging to evaluate the possible risk of a skin spot merely by looking at it and feeling it. This review article offers a thorough overview of current breakthroughs in machine learning (ML) and computer-aided diagnostics (CAD) for the aim of analysis and classification of skin cancer lesions over the past 6 years. This paper carefully reviews the whole diagnostic process: data preparation, lesion segmentation, feature extraction, feature selection, and final classification. Analyzed are many publicly accessible datasets and creative ideas including deep learning (DL) and ML integrated with computer vision, together with their impact on increasing diagnosis accuracy. Given the variety and complexity of skin lesions, even with enormous progress, there are still major obstacles. This review rigorously assesses current methods, notes areas of great challenge, and provides recommendations to direct the next research targeted at improving early detection strategies and CAD systems.

Keywords

classification / datasets / deep learning / dermoscopy / machine learning / skin cancer

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