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@article{208641,
        author = {Saranya . D and Dr.N.A.Sheela SelvaKumari},
        title = {Artificial Intelligence-Driven Early Detection and Classification of Skin Cancer Using Advanced Dermoscopic Image Analysis.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {4},
        pages = {2195-2200},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208641},
        abstract = {Skin cancer, especially malignant melanoma, is one of the major global health issues. Early detection is crucial for patient life. Although dermoscopy improved diagnostic assessments and manual interpretation, they are individually and highly dependent on clinician expertise. This paper presents a wide-ranging outline that integrates deep learning architecture, specifically Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models for automated detection of skin lesions that includes preprocessing, segmentation, and classification. The datasets include ISIC and HAM10000 with Explainable AI (XAI) techniques that are used to bridge the "black-box" gap in clinical decision-making.},
        keywords = {Skin Cancer, Deep Learning, Melanoma, Dermoscopic, Lesion.},
        month = {September},
        }