Title: A synergic deep learning approach for efficient grading of glioma via MRI images

Authors: Nirmal Yadav

Addresses: Cluster Innovation Centre, University of Delhi, New Delhi, 110007, India

Abstract: Computer-aided diagnosis using deep learning approaches has made tremendous improvements in medical imaging for automatically detecting tumor area, tumor type, and grading of the tumor. These advancements are limited due to the fact that (1) medical images are often less in quantity, leading to overfitting, and (2) significant inter-class similarity and intra-class variation between the images. The main aim of the study is to develop a deep learning base model (Zhang et al., 2018; 2019; Krizhevsky et al., 2012) as a backbone for the automatic grading of glioma tumors. The synergic deep learning (SDL) architecture enables two pre-trained models to learn from each other mutually and allows them to perform better than vanilla pre-trained models. Our study uses T1-weighted sagittal tumor magnetic resonance imaging (MRI) slices from the REMBRANDT (Scarpace et al., 2019) dataset. The proposed architecture achieves an accuracy of 98.36%, showing that the model achieves excellent performance metrics on a small dataset.

Keywords: glioma tumour grading; SDL; synergic deep learning; transfer learning; AlexNet; REMBRANDT.

DOI: 10.1504/IJCSM.2025.146077

International Journal of Computing Science and Mathematics, 2025 Vol.21 No.1, pp.1 - 13

Received: 12 Apr 2024
Accepted: 19 Oct 2024
Published online: 06 May 2025 *

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