Abstract
Magnetic Resonance Imaging (MRI) scans are crucial role in identifying brain tumors, ensuring accurate clinical diagnosis and effective personalized treatment planning to improve the chances of survival in patients. However, consistent multi-class classification of brain tumours remains a major challenge due to the considerable variability in tumor morphology and the subtle differences among multiple pathological categories. Although there have been tremendous advancements in convolutional neural networks (CNNs) and attention-based deep learning frameworks, challenges remain in achieving robustness performance across multi-class tumor datasets while maintaining interpretability for clinical use. This paper addresses these challenges, by adopting a novel multi-scale deformable attention module (MS-DAM) framework built on ResNet101. The framework is applied on the Kaggle 14-class MRI Brain tumor dataset, to enhance diagnostic accuracy and computational efficiency by capturing the global contextual and local tumor specific features. To improve generalization, hybrid augmentation strategy combined with mixup regularization has been implemented. The explainabiliy of the model is achieved through Grad-CAM and SHAP analyses. The test results of the proposed model are compared with those reported in the existing literature and superior classification accuracy and generalization are observed. The accuracy of the validation and test data set is achieved 96.89% and 99.21% respectively.
Data availability
The implementation code, training pipeline, and evaluation scripts used in this study are publicly available to ensure transparency and reproducibility of the reported results. The complete source code can be accessed through the following GitHub repository: https://github.com/bathinisiddareddy-arch/siddareddy.
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Acknowledgements
The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University, Kingdom of Saudi Arabia for funding this work through the Small Research Group Project under the grant number RGP1/21/46.
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B. Sidda Reddy - A, Ranjeet Ranjan Jha - B, Abhishek Dasore - C, Deekshitha Desur - D, Kiran Shahapurkar - E, Vineet Tirth - F, Ali Algahtani - G, Vijayabhaskara Rao Bhaviripudi - H, Gezahgn Gebremaryam - IA.B. conceptualized the work and methodology for the work, C.D. wrote the main manuscript text and formal analysis, E.F. G., V.T. and A.A supervised the work and did the review and editing of the work, H.I. did the investigation.
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Reddy, B.S., Jha, R.R., Dasore, A. et al. Multi-class classification of brain tumor using a ResNet101 backbone integrated with multi-scale deformable attention module and advanced data augmentations. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45675-y
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DOI: https://doi.org/10.1038/s41598-026-45675-y
