enhancing skin lesion segmentation with attention u-net and conditional random fields: a deep learning-based framework

Skin cancer is among the most prevalent and life-threatening diseases worldwide, and early detection significantly improves patient survival rates. in this study, we propose a deep learning-based method for the automated diagnosis and segmentation of skin lesions. the approach is built upon an enhanced u-net architecture incorporating attention mechanisms and shortcut connections to better capture lesion boundaries and contextual features. to further refine the segmentation results, conditional random fields are employed as a post-processing step, enhancing spatial coherence and boundary precision. the proposed method was evaluated on the ham10000 dataset, achieving 97.14% accuracy, a dice coefficient of 0.9394, and a jaccard index of 88.67%, demonstrating strong performance in distinguishing lesions from healthy skin tissue. with its robustness to image noise and its ability to minimize both false positives and false negatives, the model shows great potential as an effective computer-aided diagnostic tool for clinicians in the management of melanoma and other skin conditions.