One of the most frequent cancers worldwide is skin cancer, often known as cancer of the skin or SC. Although a clinical examination of skin lesions is crucial for detecting the disease's characteristics, it is limited by the time it takes and the variety of interpretations it may lead to. Machine learning (ML) and deep learning (DL) techniques have been developed to assist dermatologists in making an early and accurate diagnosis of SC, which is crucial for increasing the patient's survival rate. Here, we systematically review the literature on skin lesion classification using machine learning. Our goal is to provide newcomers to the subject with a solid basis to develop their future studies and contributions. Several online databases were searched with the use of inclusion/exclusion criteria. Documents were selected for this assessment based on their ability to provide a detailed account of the procedures taken and an accurate account of the outcomes achieved. Sixty-eight studies were selected, the vast majority of which rely on DL methods for detecting and classifying skin cancer, particularly convolutional neural networks (CNN), with a lesser number relying on ML techniques or hybrid ML/DL approaches. The papers were chosen for their usefulness in diagnosing and categorizing skin cancer. Several ML and DL methods provide state-of-the-art results in categorizing skin lesions. The promising results achieved so far bode well for the eventual use of these methods in clinical practice.
Deep-Learning, Convolutional Neural Networks (CNN), Skin Cancer, Acute lymphoblastic leukemia, X-rays, CT scans, MRI scans.
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