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{163955,
        author = {Jinay Pagariya},
        title = {Skin Lesion Detection Using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2148-2151},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163955},
        abstract = {This study presents an innovative automated skin disease detection system using Convolutional Neural Networks (CNNs). Leveraging a diverse dataset and transfer learning, the proposed model exhibits superior accuracy in identifying a wide range of dermatological conditions. Through meticulous training, data augmentation, and optimization, the CNN model demonstrates heightened sensitivity and specificity, outperforming existing methods. The system's potential integration into clinical practice holds promise for expediting diagnoses, improving patient outcomes, and optimizing healthcare resources. The study underscores the efficiency and reliability of the CNN-based approach, positioning it as a valuable tool for dermatologists and healthcare professionals in the quest for timely and accurate skin disease diagnosis.},
        keywords = {Skin lesion detection, Convolution Neural Networks, Automated diagnosis, Medical image analysis, Healthcare Optimization, Deep learning},
        month = {},
        }