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.
Reddy et al. (Fri,) studied this question.