Literature Review: Skin Cancer Types Detection Using Deep Learning

Abstract

Skin cancer ranks among the swiftly proliferating diseases globally, exacerbated by the limited resources. Timely recognition of skin cancer holds paramount importance for precise diagnosis and identification, facilitating a preventative approach overall. The incidence of melanoma, the most perilous type of cancer of the skin, is increasing. Identifying skin cancer in its initial phases poses a challenge for dermatologists. Over the past few years, both supervised and unsupervised learning assignments have extensively employed deep learning techniques. Among these, Convolutional Neural Networks (CNN) has outperformed its counterparts in tests related to object detection and classification. The algorithm used here relies on a bank of directional filters (difference of Gaussians) and explores color, directionality and topological properties of the network. Dull Razor algorithm has been used to remove artifacts such as hair as they cause difficulties in detecting pigments. Keywords— Deep learning, Convolutional Neural Networks (CNN), Color analysis, Data Augmentation, Artifacts removal, Pigment detection.