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@article{196311,
        author = {Er. Waheeda Dhokley and Ansari Usama and Biswajyoti Aown and Ansari Armaan},
        title = {Integrated Machine Learning and Optimization Techniques for Skin Cancer Detection and Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13859-13872},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196311},
        abstract = {Skin cancer is one of the most prevalent forms of cancer globally. In this paper, an integrated pipeline is proposed that combines YOLOv9 and RCNN architectures for lesion detection and classification using the ISIC 2019 and HAM10000 datasets, which are merged together. Unlike prior studies in which a single detection model is deployed, the real- time detection capability of YOLOv9 is leveraged along with the boundary precision of RCNN. Through comparative evaluation, improved diagnostic accuracy and robustness are observed across melanoma, basal cell carcinoma, and squamous cell carcinoma lesions.},
        keywords = {Skin Cancer, ISIC Dataset, RCNN, YOLOv9, Deep Learning, Medical Imaging},
        month = {April},
        }