Investigating the Application of Machine Learning and Deep Learning for Skin Cancer Detection

EasyChair Preprint no. 13153

Abstract

Skin cancer is a significant global health concern, with early detection being crucial for effective treatment and improved patient outcomes. Recent advances in machine learning (ML) and deep learning (DL) have shown promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review of the current state-of-the-art techniques and methodologies in the application of ML and DL for skin cancer detection. It explores the use of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures in analyzing dermatoscopic images and clinical data for automated skin cancer classification. Additionally, the paper discusses challenges, such as data scarcity, model interpretability, and deployment in clinical settings, and proposes potential solutions. Finally, it highlights future research directions and the potential impact of ML and DL in revolutionizing skin cancer detection and diagnosis.

Keyphrases: deep learning, Machine Language, Marchine Learning

@Booklet{EasyChair:13153,
  author = {Favour Olaoye},
  title = {Investigating the Application of Machine Learning and Deep Learning for Skin Cancer Detection},
  howpublished = {EasyChair Preprint no. 13153},

  year = {EasyChair, 2024}}