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@article{190595,
        author = {Gorle  Rohini and V.Sirisha and Gandreti Lavanya and Sanniboyina Laxmi Narayana and Piyushkumar Jain and Marada Manikanta and Prajapathi Tharun},
        title = {Automated Skin Diseases Detection Using Image Processing Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {3415-3417},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190595},
        abstract = {Skin diseases are a major global health concern affecting people of all age groups. Early and accurate diagnosis plays a crucial role in effective treatment and prevention of severe complications such as melanoma. Traditional diagnosis methods depend heavily on expert dermatologists, making the process subjective, time-consuming, and inconsistent. This paper presents an automated skin disease detection system using image processing techniques. 
The proposed approach involves preprocessing of skin images, segmentation of affected regions, extraction of meaningful texture features, and classification using a machine learning classifier. The entire system is implemented using MATLAB. Experimental results demonstrate that the proposed method provides reliable performance and can assist dermatologists as well as serve as an early diagnostic tool, especially in resource-limited environments.},
        keywords = {Skin Disease Detection, Image Processing, MATLAB, Segmentation, Feature Extraction, Classification},
        month = {January},
        }