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@article{173147,
        author = {V S S Saketh and P.Sreesa Sarma and Dr.R.Prema},
        title = {Convolutional Neural Networks in Dermal Lesion Segmentation: A Step Toward Precision Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2037-2041},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173147},
        abstract = {The ability to identify skin conditions, especially skin cancer, early on is vital for better treatment outcomes and increasing survival rates. Modern advancements like Convolutional Neural Networks (CNNs) have significantly improved the accuracy and efficiency of diagnosing skin lesions. These networks process medical images to recognize and classify lesions with remarkable detail, often detecting subtle signs of cancer that could easily be overlooked by the human eye. This early detection not only allows for faster intervention but also lightens the load on dermatologists, giving them more time to focus on complex cases. By automating much of the diagnostic process, patients benefit from quicker, more precise results and reduced waiting times. This technology not only enhances the effectiveness of healthcare systems but also ensures patients receive the timely care they need. In the end, the integration of CNNs into dermatological practices brings hope for more precise and life-saving treatments.},
        keywords = {Early detection, Skin cancer diagnosis, Convolutional Neural Networks (CNNs), Skin lesions.},
        month = {February},
        }