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{193029,
        author = {Samiksha Prakash Ambekar and Sakshi sonyabapu jadhav and Ankita Nilesh Satpute and Samiksha Prakash Ambekar and Payal Anil kothule and Waman.A.V},
        title = {Brain-Tumore-Detection-Using-Deep-Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {4715-4717},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193029},
        abstract = {Brain tumor diagnosis using MRI imaging plays a critical role in early medical intervention. This paper proposes a hybrid deep learning framework that combines Convolutional Neural Networks (CNN) with Transfer Learning for multi-class brain tumor classification. Unlike conventional binary detection systems, the proposed method classifies tumors into Glioma, Meningioma, Pituitary, and No Tumor categories. Image augmentation and fine-tuning of a pre trained ResNet50 model are implemented to enhance performance. Experimental evaluation demonstrates improved accuracy, faster convergence, and better generalization compared to traditional CNN models. The system provides an efficient and reliable solution to assist radiologists in automated brain tumor diagnosis.},
        keywords = {},
        month = {February},
        }