Self-Supervised Learning for Melanoma Skin Cancer Diagnosis: Unravelling Representations in Dermoscopic Images

The study provides an innovative system for identifying melanoma skin cancer using Dermoscopic images and self-supervised learning (SSL). Existing systems for melanoma diagnoses frequently rely on supervised learning, which requires large labelled datasets for training and might be constrained by data availability and bias. The proposed system solves these difficulties by utilizing SSL techniques, allowing the model to extract meaningful representations from input without needing explicit labels. The methodology includes data collection, preparation, and creation of a self-supervised learning system based on SimCLR and the ResNet-50 architecture. The results show that the proposed system outperforms existing systems across multiple datasets and skin types, with high accuracy (0.92), precision (0.93), recall (0.93), and F1 score (0.94). Furthermore, the approach improves interpretability and diagnostic accuracy, demonstrating its potential as a valuable tool in melanoma detection across various clinical situations. The research emphasizes the importance of SSL in tackling issues in medical image classification, particularly in dermatology, and provides a viable route for increasing melanoma detection speed and reliability.