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@article{195689,
        author = {LOGESH P and Ms Abinaya S B.Sc.,MCA.,},
        title = {SKIN CANCER DETECTION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1919-1922},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195689},
        abstract = {Skin cancer is one of the most common and potentially life-threatening diseases, where early detection plays a crucial role in effective treatment. This project presents an AI-powered system for automatic skin cancer detection using deep learning techniques. The model is built using EfficientNet-B0 architecture in PyTorch to classify dermoscopic images into nine different skin disease categories, including both benign and malignant conditions. A user-friendly Gradio interface allows users to upload skin images and receive instant predictions along with confidence scores and highlighted regions using Grad-CAM visualization. The system also provides medical guidance by suggesting consultation with dermatologists or cancer hospitals based on the prediction. By enabling fast and accurate preliminary screening, the system supports early diagnosis and improves accessibility to healthcare assistance.},
        keywords = {Skin cancer detection, Deep learning, EfficientNet-B0, Image classification, PyTorch, GradCAM, Dermoscopic images, medical image analysis, Gradio interface, Early diagnosis},
        month = {April},
        }