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@article{179512,
        author = {nusha Shekar Gouda Malipatil and Sneha S Ballari and Thaksha Prabhakar and Sneha A and Mr. Ramesh T and Ramyashree H G},
        title = {DermaDetect – AI-Based Tool For Preliminary Diagnosis Of Dermatological Manifestations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8199-8205},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179512},
        abstract = {Skin diseases are among the most common health issues worldwide, yet early and accurate diagnosis is often limited by lack of access to dermatologists. To address this, we developed DermaDetect, a web-based deep learning application that classifies skin disease images into five categories: Actinic Keratosis, Alopecia, Chondrodermatitis Nodularis, Keloids, and Molluscum Contagiosum. The system is powered by a fine-tuned MobileNetV2 model trained on labeled dermatology images. It provides instant predictions with confidence scores through a user-friendly web interface built using Gradio. This project aims to support early detection and reduce the diagnostic burden in remote or resource- constrained areas.},
        keywords = {Deep Learning, Skin Disease Detection, MobileNetV2, Image Classification, Gradio Web App, Dermatology.},
        month = {May},
        }