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Volume 13
Issue 9
10.3390/technologies13090379
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Marco Antonio Gómez-Guzmán
1,
Laura Jiménez-Beristain
2,
Enrique Efren García-Guerrero
1,*,
Oscar Adrian Aguirre-Castro
1,
José Jaime Esqueda-Elizondo
2,
Edgar Rene Ramos-Acosta
1,
Gilberto Manuel Galindo-Aldana
3,
Cynthia Torres-Gonzalez
3 and
Everardo Inzunza-Gonzalez
1,*
1
Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Transpeninsular Ensenada-Tijuana No. 3917, Ensenada 22860, Baja California, Mexico
2
Facultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Calzada Universidad No. 14418, Parque Industrial Internacional, Tijuana 22424, Baja California, Mexico
3
Laboratory of Neuroscience and Cognition, Facultad de Ciencias Administrativas, Sociales e Ingeniería, Universidad Autónoma de Baja California, Carr. Est. No. 3 s/n Col. Gutierrez, Mexicali 21700, Baja California, Mexico
*
Authors to whom correspondence should be addressed.
Technologies 2025, 13(9), 379; https://doi.org/10.3390/technologies13090379
Submission received: 30 June 2025 / Revised: 7 August 2025 / Accepted: 20 August 2025 / Published: 22 August 2025
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
Versions Notes

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 DeiT3_base_patch16_224, Xception41, Inception_v4, and Swin_Tiny_Patch4_Window7_224, 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, Swin_Tiny_Patch4_Window7_224 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.
Keywords: brain tumor classification; medical image analysis; magnetic resonance imaging (MRI); multi-class classification; computer-aided diagnosis (CAD); deep learning; transfer learning; convolutional neural networks (CNNs); vision transformers (ViT); artificial intelligence brain tumor classification; medical image analysis; magnetic resonance imaging (MRI); multi-class classification; computer-aided diagnosis (CAD); deep learning; transfer learning; convolutional neural networks (CNNs); vision transformers (ViT); artificial intelligence

Share and Cite

MDPI and ACS Style

Gómez-Guzmán, M.A.; Jiménez-Beristain, L.; García-Guerrero, E.E.; Aguirre-Castro, O.A.; Esqueda-Elizondo, J.J.; Ramos-Acosta, E.R.; Galindo-Aldana, G.M.; Torres-Gonzalez, C.; Inzunza-Gonzalez, E. Enhanced Multi-Class Brain Tumor Classification in MRI Using Pre-Trained CNNs and Transformer Architectures. Technologies 2025, 13, 379. https://doi.org/10.3390/technologies13090379

AMA Style

Gómez-Guzmán MA, Jiménez-Beristain L, García-Guerrero EE, Aguirre-Castro OA, Esqueda-Elizondo JJ, Ramos-Acosta ER, Galindo-Aldana GM, Torres-Gonzalez C, Inzunza-Gonzalez E. Enhanced Multi-Class Brain Tumor Classification in MRI Using Pre-Trained CNNs and Transformer Architectures. Technologies. 2025; 13(9):379. https://doi.org/10.3390/technologies13090379

Chicago/Turabian Style

Gómez-Guzmán, Marco Antonio, Laura Jiménez-Beristain, Enrique Efren García-Guerrero, Oscar Adrian Aguirre-Castro, José Jaime Esqueda-Elizondo, Edgar Rene Ramos-Acosta, Gilberto Manuel Galindo-Aldana, Cynthia Torres-Gonzalez, and Everardo Inzunza-Gonzalez. 2025. "Enhanced Multi-Class Brain Tumor Classification in MRI Using Pre-Trained CNNs and Transformer Architectures" Technologies 13, no. 9: 379. https://doi.org/10.3390/technologies13090379

APA Style

Gómez-Guzmán, M. A., Jiménez-Beristain, L., García-Guerrero, E. E., Aguirre-Castro, O. A., Esqueda-Elizondo, J. J., Ramos-Acosta, E. R., Galindo-Aldana, G. M., Torres-Gonzalez, C., & Inzunza-Gonzalez, E. (2025). Enhanced Multi-Class Brain Tumor Classification in MRI Using Pre-Trained CNNs and Transformer Architectures. Technologies, 13(9), 379. https://doi.org/10.3390/technologies13090379

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