A Comprehensive Study on Machine Learning and Deep Learning Models for Skin Cancer Detection

Abstract

Lesions, which can be benign or malignant, are abnormal tissue areas that occur on the skin or within internal organs. Malignant lesions, including types of skin cancer like Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), and Melanoma (MM), pose significant health risks and necessitate early detection and treatment. Machine learning (ML) and deep learning (DL) algorithms have become crucial tools for identifying and diagnosing skin cancer. This paper discusses the various forms of skin cancer, the application of ML algorithms such as LSTM, ARIMA, SVM, KNN, and Decision Trees in skin cancer detection, and the role of DL methods like CNN, transfer learning, and reinforcement learning in improving diagnostic accuracy. Through a literature survey, we explore recent advancements in skin cancer detection using ML and DL technologies, this research highlights the potential of integrating ML and DL into routine dermatological practice, offering a powerful tool for enhancing diagnostic accuracy, reducing the rate of unnecessary biopsies, and ultimately improving patient outcomes. Future work will aim to address the challenges, refine model performance, and enhance practical applicability in clinical environments.