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@article{162927,
        author = {Kiran Doiphode and Madhuri Shinde and Pranali Surve and Sofiya Sutar and pallavi Hajare},
        title = {Skin Disease Detection using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {453-456},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162927},
        abstract = {The project aims to develop a skin disease detection system using machine learning techniques. Leveraging image processing and classification algorithms, the system will analyze dermatological images to accurately identify various skin conditions. By employing convolutional neural networks (CNNs) trained on extensive datasets of labeled images, the system will learn to recognize patterns and features indicative of different diseases such as eczema, psoriasis, melanoma, and others. The proposed system seeks to provide a non-invasive, cost-effective, and efficient solution for early diagnosis and treatment recommendation, aiding healthcare professionals in timely intervention. Through the integration of machine learning algorithms with a user-friendly interface, the system will enable easy access for both medical practitioners and patients, potentially reducing the burden on dermatologists and improving healthcare outcomes. This project holds promise in revolutionizing dermatological diagnostics, contributing to better patient care and management of skin diseases.},
        keywords = {Dermatology, Skin Disease, Convolutional Neural Network},
        month = {},
        }