Published Paper Details:

Skin disease and skin cancer diagnosis primarily depend on manual examination by dermatologists, a process that is often time- consuming, subjective and susceptible to human error. Delays in detection and limited access to specialists can result in severe health complications. To address these limitations, this work presents an AI-powered diagnostic system for automated skin lesion detection and classification using advanced deep learning techniques. The system integrates real-time image analysis, precise segmentation of affected regions and automated classification to facilitate early and accurate disease identification. A Swin UNet architecture is employed to achieve high-precision lesion boundary masking, a critical component in accurate dermoscopic diagnosis. To enhance classification accuracy, a transformer-based model specifically the Swin Transformer is implemented to capture fine-grained lesion features effectively. The ultimate objective is to refine the system for reliable clinical deployment, ensuring consistent support for early and accurate skin lesion diagnosis. Through the integration of AI and deep learning, this approach seeks to enhance diagnostic precision and expand access to high-quality dermatological care.