Identification of Skin Cancer Using Machine Learning

Abstract

Skin cancer, characterized as a chronic disease, demands time-consuming and costly medical tests for accurate detection, thereby introducing risks associated with treatment delays. Acknowledging the critical need for efficient skin cancer detection, this thesis endeavors to make a significant contribution by proposing an advanced deep learning methodology. The innovative approach involves enhancing the ResNet model with SE modules and integrating a maximum pooling layer within the ResBlock shortcut connection. In comparison to established models (ResNet-50, SENet, DenseNet, and GoogleNet), the proposed method surpasses them in accuracy, parameter efficiency, and computation speed, achieving an impressive average recognition accuracy of 97.48% on a comprehensive 2142-image dataset. This transformative solution aspires to not only revolutionize skin cancer detection but also elevate the standard of patient care in this critical domain.