Skin cancer is one of the most common and life-threatening diseases worldwide, where early and accurate diagnosis is crucial for effective treatment. Traditional diagnostic methods mainly rely on visual examination by dermatologists, which can be time-consuming, subjective, and prone to human error. To address these limitations, this project proposes an automated skin cancer detection system using machine learning and deep learning techniques to assist in early diagnosis and reduce manual effort. The project focuses on a comparative analysis of various machine learning models for multi-label skin cancer classification, where a single skin lesion image may belong to multiple disease categories. Traditional models such as SVM and Random Forest and deep learning models including Convolutional Neural Networks and transfer learning-based architectures like ResNet and DenseNet.
Mahesh et al. (Thu,) studied this question.