On the Performance of CNN and GAN models for Melanoma Classification

Melanoma is the most harmful form of skin cancer and they emerge from melanocytes, a type of pigment-producing cells. Melanomas usually occur on the skin; they mostly form around the back for men, whereas the common place for women are legs. Once formed, symptoms of melanoma might include an increase in the size of some mole with irregular edges, or itchiness or changes in its color, etc. This paper reviews the latest research works on melanoma classification using CNN. The performance analysis of the state-of-the-art methods is done focusing only on the binary classification of melanoma. The dataset used for the performance analysis is based on SIIM-ISIC Melanoma Classification Challenge 2020, where the task was to identify melanoma using images of skin lesions by predicting a binary target for each image. The main intent of this study is to review the state-of-the-art deployments which identifies the recent research trends, challenges, and newer domains, like Generative Adversarial Networks (GAN) for melanoma classification and scrutinize the existing solutions for melanoma detection.