Shubhra Chinchmalatpure,
Pratiksha Chafle,
- Research Scholar,, Department of Computer Science, GH Raisoni University, Dhoda Borgaon, Madhya Pradesh,, India
- Assistant Professor,, Department of Computer Science, GH Raisoni University, Dhoda Borgaon, Madhya Pradesh, India
Abstract
Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise reduction. The proposed CNN learns discriminative features from the image without any manual intervention in feature engineering. The proposed CNN model is trained and tested on a publicly available benchmark dataset for MRI images. The performance of the model was assessed using accuracy, precision, recall, F1 score, and confusion matrix. Experimental results revealed that the proposed CNN model attained a classification accuracy of 94.6%. This shows that the proposed model performs better in brain tumor diagnosis compared to traditional machine learning algorithms like Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN).
Keywords: Brain tumor detection, magnetic resonance imaging (MRI), deep learning, convolutional neural networks (CNN), medical image analysis
[This article belongs to International Journal of Brain Sciences ]
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International Journal of Brain Sciences
| Volume | 03 | |
| Issue | 02 | |
| Received | 07/05/2026 | |
| Accepted | 29/05/2026 | |
| Published | 20/07/2026 | |
| Publication Time | 74 Days |