AI-ASSISTED SKIN LESION DIAGNOSIS

Automated skin cancer diagnosis with a deep learning-based system that uses Convolutional Neural Networks (CNN) for classification and U-Net for precise lesion segmentation. To enhance patient outcomes, early and precise detection is essential for skin cancer, especially melanoma. The suggested strategy improves generalization by utilizing transfer learning and extensive dermoscopic datasets like the ISIC Archive and HAM10000. CNN divides skin lesions into benign and malignant groups, but U-Net precisely divides the afflicted areas, allowing for improved clinical evaluation and localization. The model uses post-processing methods, hyperparameter adjustment, and data augmentation to increase its accuracy and resilience. The system helps dermatologists diagnose patients quickly and non-invasively by automating feature extraction and segmentation, which improves clinical operations. Incorporating explainable AI (XAI) for interpretability and real-time deployment are examples of future improvements.