Copyright © 2026 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{186362,
        author = {Amit Kumar Bachcha Jha},
        title = {AI-Based Image Classification for Early Detection of Skin Cancer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1218-1219},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186362},
        abstract = {Skin cancer is one of the most prevalent types of cancer globally, with melanoma being the most fatal form if not detected early. Accurate and early diagnosis significantly improves patient outcomes and survival rates. Traditional diagnostic methods rely heavily on clinical examination and dermoscopic image analysis by dermatologists, which can be subjective and time-consuming. This research explores the development of an Artificial Intelligence (AI)-based system utilizing Convolutional Neural Networks (CNNs) to classify dermoscopic images of skin lesions into benign and malignant categories. The study aims to improve diagnostic accuracy, reduce human error, and provide an interpretable model through explainable AI techniques such as Grad-CAM visualization.},
        keywords = {Artificial Intelligence, Skin Cancer, Deep Learning, Convolutional Neural Networks, Explainable AI, Dermoscopy, Medical Imaging.},
        month = {November},
        }