Deep Learning-Driven Skin Cancer Diagnosis: Advanced Neural Network Architectures for Automated Detection and Classification in Dermatology

Abstract

This paper kind of explores how deep learning can be used for detecting and diagnosing skin cancer, using deeper CNN setups like VGG-16, ResNet50 and MobileNetV2. Since skin cancer occurrence is rising globally, there is this growing need for diagnostic solutions that are really effective and precise. Transfer learning is actually employed here to push the performance of the models, so they can reach effective classification results for thermoscopic images, with accuracy that looks comparable or even better than those of human experts. The paper also lays out the pipeline in a straightforward way, for example data gathering, data preprocessing, model training, and evaluation, though it keeps a special focus on how the deep learning models are customized so they can do skin cancer detection. Based on the experimental results, the diagnostic accuracy jumps quite a lot, where MobileNetV2 ends up doing the best, reaching around 97% accuracy and still staying efficient regarding computation, plus resource use. That efficiency is part of why it can be used in real-time, with less delay and less overhead, in practice. Finally the research looks into additional concerns like adversarial robustness, and also the incorporation of non-image clinical data, to further lift diagnostic performance. Overall, the findings suggest transformative about artificial intelligence for medical diagnostics, meaning it could improve early detection and support better treatment outcomes for skin cancer patients.