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@article{194148,
        author = {Mr. C. Charaneswara Reddy and Ms. C. Madhavi Latha and Mr. A. Sudheer Kumar Reddy and Mr. D. Aravind and Mr. G. Haribabu},
        title = {Robust Multi-Class Skin Lesion Classification Using CNN and Advanced Image Processing Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2490-2493},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194148},
        abstract = {Skin lesions are among the most common dermatological conditions worldwide, and early detection plays a crucial role in preventing severe health complications such as melanoma and other skin cancers. Traditional diagnostic methods rely heavily on dermatologists' expertise and visual inspection, which can be time-consuming and subject to human error. In recent years, deep learning techniques have shown significant potential in medical image analysis. This paper proposes a robust multi-class skin lesion classification system using Convolutional Neural Networks (CNN) combined with advanced image processing techniques. The proposed framework performs preprocessing operations such as noise removal, normalization, resizing, and contrast enhancement to improve image quality and feature extraction. A CNN-based model is employed to automatically learn hierarchical features from dermoscopic images and classify them into multiple skin disease categories. The experimental results demonstrate that the proposed model achieves high classification accuracy and reliable performance. The system can assist dermatologists by providing a fast and efficient computer-aided diagnostic tool for early skin disease detection.},
        keywords = {Skin Lesion Classification, CNN, Image Processing, Deep Learning, Medical Image Analysis.},
        month = {March},
        }