Examining the Effectiveness of Machine Learning and Deep Learning Techniques for Skin Cancer Detection

EasyChair Preprint 13138

Abstract

Skin cancer is a significant public health concern, with early detection being critical for successful treatment. Machine learning (ML) and deep learning (DL) techniques have shown promise in improving the accuracy and efficiency of skin cancer detection. This paper presents a comprehensive review of the effectiveness of ML and DL techniques in the detection of skin cancer. We discuss various approaches, including convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble methods, highlighting their strengths and limitations. We also examine the challenges and opportunities in the field, such as data scarcity, model interpretability, and integration into clinical practice. Finally, we propose future research directions to enhance the performance and applicability of ML and DL in skin cancer detection.

Keyphrases: deep learning, health care, machine learning

@booklet{EasyChair:13138,
  author    = {Dylan Stilinski and Abill Robert},
  title     = {Examining the Effectiveness of Machine Learning and Deep Learning Techniques for Skin Cancer Detection},
  howpublished = {EasyChair Preprint 13138},
  year      = {EasyChair, 2024}}