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Detection and analysis of brain tumours from Magnetic Resonance Imaging (MRI) scans are essential for the early detection and treatment of brain cancers. Manual analysis of the MRI images is a time-consuming process for radiologists, as there are possibilities of human errors, especially while handling large volumes of medical images. An automatic brain tumor image classification method utilizing Deep Learning algorithms is proposed to counter this problem. For this project, three different models of deep learning are applied. First, a personal solution of the Convolutional Neural Network (CNN) architecture is developed as a basic model to identify the spatial features of the MRI images. Second, VGG16, a transfer learning model developed earlier for other large-scale image databases, is utilized to tap into the experience gained from handling large-scale image databases. Third, ResNet50, a more complex model of the residual network architecture, is applied to overcome the problem of a disappearing gradient during the backward propagation process to achieve better results for the classification process of the brain tumor dataset. The proposed system can also provide real-time prediction, and any new MRI image can be uploaded by the user, showing the predicted tumor type with the confidence score. The experimental findings prove that the transfer learning models achieve higher performance compared to the basic CNN, among which ResNet50 obtained the top result. This work verifies deep learning and transfer learning in medical image analysis and delivers a trustworthy way of automatic brain tumor classification.
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Copyright © 2026, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: C Lokeshwari, lokeshwari2528@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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