Performance Benchmarking of Deep Learning Models for Skin Lesion Classification in Resource-Constrained Environments

Skin cancer stands as the most prevalent cancer worldwide because health professionals detect new instances regularly in millions annually. The high curability of skin cancer becomes less effective when patients receive their diagnosis too late, which results in elevated mortality numbers worldwide. The research examines deep learning algorithm deployment for improved dermatological examination via skin cancer identification systems. The study evaluates how Convolutional Neural Networks (CNNs), particularly ResNet-50 and SqueezeNet, maintain diagnostic accuracy by analyzing medical images. The assessment uses diverse skin datasets that include various symptoms of cancer alongside different skin types. The study describes CNN design principles and explains data optimization strategies and augmentation approaches that boost system execution. This paper provides a comparative examination of deep learning models that includes an investigation of their interpretability to determine which features generate precise detection results. The study demonstrates that deep learning presents strong potential to become a diagnostic tool that aids physicians in early skin cancer detection and helps them make better clinical choices. The research contributes significant knowledge for improving medical image analysis techniques and technological progress in dermatology.