Title: A novel approach of multi-classification of brain tumour with MRI images using transfer learning

Authors: Rashmi Jolhe; Sudhir D. Sawarkar

Addresses: Department of Information Technology, Datta Meghe College of Engineering, Airoli, Navi Mumbai, Maharashtra, India ' Department of Computer Engineering, Datta Meghe College of Engineering, Airoli, NaviMumbai, Maharashtra, India

Abstract: Brain tumour identification via MRI is vital for early diagnosis and treatment planning, yet manual detection is time-consuming and accuracy varies. Our framework employs transfer learning, using fine-tuned pre-trained networks to extract deep features from MR images. We fine-tuned and evaluated six pre-trained neural networks for a brain tumour classification challenge, aiming to enhance computation efficiency and mitigate overfitting risks. Standardised techniques were applied to tune model weights and extract relevant features, tailored for the 4-class tumour categorisation task. Both quantitative measures like classification accuracy and convergence speed, and qualitative aspects like required training resources, were analysed. Findings highlight the promise of properly fine-tuned pre-trained models to boost accuracy and efficiency for critical healthcare tasks. This evaluation establishes transfer learning as impactful for adapting computer vision models to real medical applications. Achieving 98.74% accuracy, our model addressed overfitting issues and yielded favourable computational results.

Keywords: deep learning; magnetic resonance imaging; MRI; glioma; meningioma and pituitary; perfusion MRI.

DOI: 10.1504/IJCVR.2026.156209

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.3, pp.297 - 315

Accepted: 06 May 2024
Published online: 08 Sep 2026 *

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