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Research Article |

A Lightweight CNN Architecture for Efficient Brain Tumor Detection in MRI Scans

Author(s): Yasser Nizamli1,2*, Anton Filatov1, Weaam Fadel3, Yulia Shichkina3, Kinda Mreish4,5

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 2

Publisher : FOREX Publication

Published : 18 June 2025

e-ISSN : 2347-470X

Page(s) : 296-305

DOI: https://doi.org/10.37391/IJEER.130213

Abstract

The intricate morphology of brain tumors poses significant diagnostic challenges in MRI interpretation. While AI-driven systems offer potential for automation, balancing accuracy with computational efficiency remains critical for clinical adoption. This work introduces a lightweight convolutional neural network optimized for brain tumor detection and classification in MRI scans. The architecture’s design emphasizes a systematic exploration of layer-ordering strategies, with experiments revealing that batch normalization in post-activation mode (Post-BN) outperforms Pre-BN in training stability and classification accuracy. Contrary to expectations, integrating shortcut connections for residual learning demonstrated negligible performance gains. Evaluated on the Figshare (multi-class) and Br35H (binary) datasets, the model achieves state-of-the-art accuracy while maintaining resource efficiency through minimized parameters and FLOPs. These findings highlight the importance of strategic layer ordering over architectural complexity in deep learning for medical imaging, offering a framework for efficient and reliable tumor detection that could generalize to other vision-based diagnostic tasks.

Keywords: Brain tumor detection,medical imaging, MRI images, Deep learning, Lightweight CNN, Batch normalization, Residual connections, Figshare dataset, Br35H dataset.




Yasser Nizamli, Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia; Department of Computer Engineering and Automatic Control, Latakia University, Latakia, Syria;

Anton Filatov, Department of Software Engineering and Computer Applications, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia;

Weaam Fadel, Department of Computer Engineering, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia;

Yulia Shichkina, Department of Computer Engineering, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia;

Kinda Mreish, Department of Computer Engineering, Aleppo University, Aleppo, Syria; Department of Information Systems, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

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Yasser Nizamli, Anton Filatov, Weaam Fadel, Yulia Shichkina and Kinda Mreish (2025), A Lightweight CNN Architecture for Efficient Brain Tumor Detection in MRI Scans. IJEER 13(2), 296-305. DOI: 10.37391/IJEER.130213.