Deep Learning for Skin Cancer Classification Using Data Augmentation and Pre-Trained CNN Models

Abstract

Skin cancer is one of the most common forms of cancer worldwide, and its early detection is critical for effective treatment and improved survival rates. In recent years, advancements in Artificial Intelligence (AI), particularly deep learning, have shown great promise in enhancing the accuracy and efficiency of medical image analysis. This study aims to develop a non-invasive, AI-powered diagnostic system for the early detection and classification of skin cancer using dermoscopic images. The proposed approach leverages deep learning techniques to automatically recognize and differentiate between various types of skin lesions, including both malignant and benign cases. The project focuses on the end-to-end development of this system, beginning with the acquisition and preprocessing of a publicly available dataset of annotated skin lesion images. The images undergo enhancement and normalization processes to improve model performance. Pre-trained Convolutional Neural Network (CNN) architecture is designed and trained to classify the lesions based on visual features. The model’s performance is evaluated using an independent validation set. By providing a reliable and accessible screening tool, this work aims to support dermatologists in early diagnosis and decision-making, potentially reducing the burden on healthcare systems and improving patient care. The study demonstrates the potential of integrating deep learning into medical diagnostics for skin cancer, paving the way for future research and clinical implementation.