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@article{199065,
        author = {Athira K S and Angel Cyril and Aswathy S and Dhanalakshmi V J},
        title = {SKIN CANCER DETECTION USING GRAD-CAM IN XAI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12781-12786},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199065},
        abstract = {Skin cancer is one of the most rapidly increasing diseases worldwide, and early detection is critical for improving survival rates. This paper presents an explainable artificial intelligence (XAI)-based system for accurate skin cancer detection using deep learning techniques. The proposed model utilizes a pre-trained ResNet50 convolutional neural network for feature extraction and classification of dermoscopic images. To enhance transparency and trust in predictions, explainability methods such as Grad-CAM and Integrated Gradients are incorporated, enabling visualization of important regions influencing the model’s decisions. Additionally, clinical analysis based on the ABCDE rule is integrated to align AI predictions with dermatological practices. The system achieves high classification accuracy while providing interpretable results, making it a reliable decision-support tool for early diagnosis. This approach bridges the gap between deep learning models and real-world clinical applications.},
        keywords = {Skin Cancer Detection, Deep Learning, ResNet50, Explainable AI, Grad-CAM, Integrated Gradients.},
        month = {April},
        }