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@article{198418,
        author = {Enjamuri Mohan and D Bala Venkata Sai and A Venkata Charan},
        title = {Early Brain Tumor Detection Using XAI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11198-11202},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198418},
        abstract = {Brain tumors are among the most life-threatening neurological disorders, and their early detection plays a crucial role in improving patient survival rates and treatment planning. Recent advancements in Artificial Intelligence (AI), particularly deep learning techniques, have shown significant potential in automated medical image analysis. Convolutional Neural Networks (CNNs) are widely adopted for analyzing Magnetic Resonance Imaging (MRI) scans due to their capability to learn hierarchical and discriminative features. However, conventional deep learning models often function as black-box systems, limiting their interpretability and clinical trust. To address this limitation, this paper proposes an AI-based framework for early brain tumor detection using MRI images integrated with Explainable Artificial Intelligence (XAI) techniques. The proposed system utilizes a fine-tuned VGG-16 CNN architecture for accurate tumor classification across four categories and incorporates Gradient-weighted Class Activation Mapping (Grad-CAM) to generate visual explanations highlighting tumor-affected regions. Experimental results on the publicly available Kaggle Brain Tumor MRI Dataset demonstrate a classification accuracy of 96.5%, with Grad-CAM visualizations confirming clinically meaningful tumor localization, thereby supporting reliable and transparent clinical decision-making.},
        keywords = {Artificial Intelligence, Brain Tumor Detection, MRI Images, Deep Learning, CNN, Grad-CAM, Explainable AI, XAI, VGG-16, Healthcare, Medical Imaging.},
        month = {April},
        }