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@article{173515,
        author = {S.Periyasamy and Dr.R.Sri Devi},
        title = {Skin Disease Diagnosis using CNN Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {965-967},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173515},
        abstract = {A skin disease diagnosis involves identifying and understanding various conditions affecting the skin, which may include infections, inflammatory diseases, autoimmune disorders, or skin cancers. Accurate diagnosis is essential for effective treatment and management. The machine assisted approach for detection of disease is at the same time more efficient. Deep learning is an artificial intelligence operation that emulates the working of human brain in organizing data and designing patterns for decision making. Most modern deep learning models are based on artificial neural networks categorically convolutional neural networks. In this paper we developed a unique deep learning architecture which focuses in the timely evaluation of skin Disease. The model could classify the Ezema, Urticaria and Normal class with 70%accuracy. The proposed deep CNN model could classify the melanoma types into benign class or malignant class. In this work, a less complicated model is used and the accuracy obtained was around 70%. The future extension to this work includes improving the prediction accuracy by parameter tuning, remodeling the network to multiclass case, which could detect different categories of skin Disease.},
        keywords = {Convolution Neural Network, Deep Learning, Skin Disease Prediction},
        month = {March},
        }