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@article{178241,
        author = {Shraddha Chaudhari and Pallavi Chaudhari and Megha Suryawanshi and Karina Chavhan and Kalyani Deshmukh and Rakesh Jambhulkar},
        title = {SKIN DISEASE DETECTION SYSTEM USING CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4064-4068},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178241},
        abstract = {This research focuses on the application of advanced data mining techniques and machine learning models, particularly Convolutional Neural Networks (CNNs), for the detection and classification of skin diseases. The study addresses the critical need for accurate and timely diagnosis of skin conditions such as melanoma, eczema, and psoriasis. Leveraging a comprehensive dataset of thermoscopic images, the CNN model is trained to extract and analyze features, enabling reliable classification of various skin diseases. The objective is to enhance the diagnostic process, reduce human error, and provide a cost-effective solution that supports healthcare professionals and dermatologists.
Skin disorders are among the most prevalent health concerns globally, impacting individuals of all age groups. Early detection and proper diagnosis are key to successful treatment, but specialized dermatological services are usually restricted, especially in rural and underdeveloped areas. This project seeks to bridge this divide by building an automated Skin Disease Detection System based on Convolutional Neural Networks (CNNs).},
        keywords = {CNN, Skin Disease Detection, Deep Learning, Image Classification, Medical Diagnosis.},
        month = {May},
        }