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@article{173601,
        author = {Mr.K.Parvateesam and R.Mahesh and P.s.s.m.swamy and P.bharath Kumar},
        title = {Skin Disease Detection System Using Convolution Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {946-952},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173601},
        abstract = {The” Skin Disease Detection System Using Con- volutional Neural Network” is designed to accurately clas- sify various skin diseases through advanced image processing techniques. The process begins with the acquisition of input images, followed by several pre-processing steps to enhance image quality. Augmentation techniques such as rotation, flipping, and zooming are applied to increase the diversity of the training dataset and improve the model’s robustness. The core of the system is a carefully designed Convolutional Neural Network (CNN) architecture, optimized for skin disease classification. The dataset is split into training, validation, and testing sets, with approximately 70-80% allocated for training and 10-15% for validation. This ensures a well-rounded model capable of generalizing to new data. The final classification step involves identifying specific skin diseases, including Actinic keratosis, Dermatofibroma, Melanoma, and Squamous cell carcinoma. This system aims to assist dermatologists in early and accurate diagnosis, ultimately improving patient outcomes through timely and precise treatment.},
        keywords = {Skin Disease Dataset, Image Processing Tech- niques, Deep Learning Techniques, Convolution Neural Net- work, Classification, Accuracy.},
        month = {March},
        }