Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{174480,
        author = {Wasim Raja A and Harith Kavish S and Pranav Krishna S and Santhiya T and Sanjay R},
        title = {SkinNet Analyzer: A Deep Learning-Based Skin Disease Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4437-4439},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174480},
        abstract = {Skin diseases affect millions worldwide, requiring timely diagnosis and effective treatment. Traditional methods rely on dermatologists, which may not be accessible, especially in remote areas with limited healthcare facilities. SkinNet Analyzer is a deep learning-based classification system using EfficientNet, ResNet, and MobileNet to enhance diagnostic accuracy. It integrates symptom-based confirmation and severity estimation, along with an AI chatbot for patient engagement and personalized recommendations. Experimental results demonstrate an accuracy of 86.7%, showcasing its potential in clinical and telemedicine applications. Future improvements include dataset expansion, real-time deployment, and enhanced model interpretability.},
        keywords = {},
        month = {March},
        }