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This is a In Vitro Study study (also classified as: Observational).

August 22, 2025Open Access

Enhanced Multi-Class Brain Tumor Classification in MRI Using Pre-Trained CNNs and Transformer Architectures

Key Points

Accuracy of 99.24% was achieved in classifying four brain tumor types using a deep learning model—indicating high diagnostic reliability.
The analysis used 7023 MRI images, with models trained on varying data subsets, showing robust performance across tumor classes.
Deep learning models such as CNNs and transformers were employed using a stratified five-fold cross-validation approach for validation.
This work emphasizes the feasibility of deploying AI-based diagnostic tools in clinical settings, aiming for optimized patient outcomes.

Abstract

Early and accurate identification of brain tumors is essential for determining effective treatment strategies and improving patient outcomes. Artificial intelligence (AI) and deep learning (DL) techniques have shown promise in automating diagnostic tasks based on magnetic resonance imaging (MRI). This study evaluates the performance of four pre-trained deep convolutional neural network (CNN) architectures for the automatic multi-class classification of brain tumors into four categories: Glioma, Meningioma, Pituitary, and No Tumor. The proposed approach utilizes the publicly accessible Brain Tumor MRI Msoud dataset, consisting of 7023 images, with 5712 provided for training and 1311 for testing. To assess the impact of data availability, subsets containing 25%, 50%, 75%, and 100% of the training data were used. A stratified five-fold cross-validation technique was applied. The CNN architectures evaluated include DeiT3baseₚatch16₂24, Xception41, Inceptionᵥ4, and SwinTinyPatch4Window7₂24, all fine-tuned using transfer learning. The training pipeline incorporated advanced preprocessing and image data augmentation techniques to enhance robustness and mitigate overfitting. Among the models tested, SwinTinyPatch4Window7₂24 achieved the highest classification Accuracy of 99. 24% on the test set using 75% of the training data. This model demonstrated superior generalization across all tumor classes and effectively addressed class imbalance issues. Furthermore, we deployed and benchmarked the best-performing DL model on embedded AI platforms (Jetson AGX Xavier and Orin Nano), demonstrating their capability for real-time inference and highlighting their feasibility for edge-based clinical deployment. The results highlight the strong potential of pre-trained deep CNN and transformer-based architectures in medical image analysis. The proposed approach provides a scalable and energy-efficient solution for automated brain tumor diagnosis, facilitating the integration of AI into clinical workflows.

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