Abstract:
One of the most dangerous types of skin cancer is malignant melanoma. Early diagnosis,
according to modern dermatology, is critical for lowering mortality rates and ensuring
that patients receive less invasive therapies. For the early identification of skin lesions,
computer-aided diagnostic (CAD) systems are becoming more popular. These systems
are made up of various phases that must be selected based on the properties of digital
images in order to produce a correct diagnostic. Acquisition, pre-processing,
segmentation, feature extraction and selection, and finally classification of dermoscopic
images all provide problems that must be met and conquered in order to improve
automatic diagnosis of deadly tumors like melanoma. The categorization phase is
particularly delicate, and a number of machine learning techniques have been presented
over time to address this problem more effectively. The many machine learning
approaches that have been proposed and that provide inspiration for the creation of
effective frameworks are discussed in this study.
