Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images

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Year : 2026 | Volume : 3 | 02 | Page :
    By

    Shubhra Chinchmalatpure,

  • Pratiksha Chafle,

  1. Research Scholar, G H Raisoni University Saikheda, MP, India
  2. Assistant Professor, G H Raisoni University Saikheda, MP, 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.

How to cite this article:
Shubhra Chinchmalatpure, Pratiksha Chafle. Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images. International Journal of Brain Sciences. 2026; 03(02):-.
How to cite this URL:
Shubhra Chinchmalatpure, Pratiksha Chafle. Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images. International Journal of Brain Sciences. 2026; 03(02):-. Available from: https://journals.stmjournals.com/ijbs/article=2026/view=247786


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Ahead of Print Subscription Review Article
Volume 03
02
Received 07/05/2026
Accepted 29/05/2026
Published 27/06/2026
Publication Time 51 Days
43

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