Convolutional Neural Networks in Dermatology: Skin Cancer Detection and Analysis

Abstract Skin cancer, a highly prevalent form of cancer, necessitates early and accurate detection to improve patient outcomes. Traditional diagnostic methods, including visual examination and biopsies, are often subjective and invasive. This paper investigates the application of Convolutional Neural Networks (CNNs) in the automated detection and classification of skin cancer, offering a non-invasive and reliable alternative. We provide an in-depth overview of CNN architecture, covering key components such as convolutional layers, pooling layers, activation functions, and the backpropagation algorithm. Additionally, we address critical aspects of dataset preparation, including image preprocessing, augmentation techniques, and the use of large, annotated datasets to enhance model performance.