Title: Automated brain tumour identification through MRI data analysis based on convolutional neural networks

Authors: Amandeep Kaur; Kuldeep Singh; Prabhpreet Kaur

Addresses: Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India ' Department of Electronics Technology, Guru Nanak Dev University, Amritsar, India ' Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India

Abstract: Brain tumour classification is a significant research area in medical imaging. Manual examination of MRI scans is time-consuming, laborious, and may lead to imprecise findings. With the growth of artificial intelligence, automated methods are increasingly used for accurate detection of different brain tumour types. This paper presents a computer-aided diagnostic technique based on a 16-layer CNN architecture for precise tumour classification. The MR images are first resized and normalised, followed by dataset balancing using a hybrid SMOTE-edited nearest neighbour method. The balanced images are then fed into the proposed CNN model. A CNN-based feature extractor is also used with machine-learning classifiers including random forest, kNN, SVM, naïve Bayes, and decision tree. Experimental results show that the proposed model achieves 98.88% accuracy for binary tumour detection and 97.83% for three-class classification, demonstrating its efficiency in detecting and identifying brain tumour types.

Keywords: brain tumour; deep learning; healthcare; machine learning; magnetic resonance imaging; MRI.

DOI: 10.1504/IJBET.2026.153222

International Journal of Biomedical Engineering and Technology, 2026 Vol.50 No.3, pp.225 - 251

Received: 19 Sep 2024
Accepted: 17 Mar 2025
Published online: 29 Apr 2026 *

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