Deep Learning Approaches for Early Detection of Skin Cancer Disease Detection Using Segmentation and Classification with Severity Analysis

The utmost prevalent type of the disease, skin cancer claims millions of lives every year, poses a serious public health threat particularly melanoma, which is often fatal if not detected early. Early diagnosis is crucial, yet traditional methods frequently fall short due to image quality limitations and the complexity of visual differentiation. With an emphasis on severity analysis, this study presents a sophisticated deep learning methods for skin cancer segmentation and classification. In order to improve quality and enable more accurate analysis, sophisticated picture pre-processing techniques are used to reduce noise while maintaining important characteristics. The refined images are then analyzed using a two-phase Self-Attention-based Hierarchical Capsule Network, which effectively extracts intricate patterns. Feature selection is optimized using the Tent Chaotic-based Walrus Optimization Algorithm (TCWOA), minimizing computational complexity. For segmentation, the Progressive Attention-based Multi-scale Hierarchical Residual Swin Transformer (PA-HRST) model is utilized. Classification is performed using the Global Attention-based Multilevel Semantic Knowledge Alignment Distillation Network (GA-MSKAD), accurately identifying seven skin cancer types. Finally, severity is predicted using the Residual Lasso Logistic Regression (RLLR) model. Using the HAM10000 dataset, which consists of 10,015 dermoscopy images from seven classes, the method shows its efficacy in detecting and forecasting skin conditions with a high testing accuracy of 99.18%. This comprehensive approach from image enhancement to severity assessment offers a significant improvement over conventional diagnostic tools. For future work, incorporating model interpretability, diverse datasets, and clinical metadata will be essential to further optimize results and support real-world medical applications.