An Improved VGG Based Deep Learning Framework for Glioma Classification in Brain MRI Images

Document Type : Original Article

Author
Asieh Khosravanian
Assistant Professor, Department of Computer Engineering, University of Larestan, Lar, Iran
10.48305/him.2026.45796.1348
Abstract
Introduction: Early detection of brain tumors can play a significant role in treatment planning. In brain magnetic resonance imaging (MRI), there are considerable similarities between healthy and cancerous tissues, making accurate tumor detection a major challenge. Furthermore, manual classification of these images is time‑consuming and prone to human error; therefore, developing automated classification methods can provide substantial support to physicians.

Methods: In this study, a novel deep neural network architecture is proposed for automatic classification of brain tumors in MRI images. In the proposed method, the VGG‑16 deep neural network architecture has been redesigned. To preserve the spatial resolution of features in deeper layers and to enable detection of small tumor regions as well as identification of ambiguous boundaries, the max‑pooling layer in the original VGG‑16 architecture was removed.

Results: The proposed method was evaluated using the BraTS2020 dataset with a 10‑fold cross‑validation approach. The evaluation results, based on the metrics Accuracy, Precision, Recall, Specificity, F1 Score, and AUC, showed that the proposed architecture achieved better performance compared to the original VGG‑16 neural network. Specifically, it attained values 0.9512 ± 0.0035, 0.9739 ± 0.0026, 0.9524 ± 0.0044, 0.9490 ± 0.0051, 0.9630 ± 0.0027, and 0.9514 ± 0.0044, respectively, for the mentioned metrics.

Conclusion: The results obtained from the proposed method in this study confirm that preserving spatial information and identifying ambiguous boundaries can play a significant role in improving classification accuracy. In addition, the applied modifications led to faster convergence and reduced over‑downsampling.

Highlights

 1. Louis DN, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncol. 2021;23(8):1231–1251 .

2. Zahoor MM, Khan SH, Alahmadi TJ, et al. Brain tumor MRI classification using a novel deep residual and regional CNN. Biomedicines. 2024;12(7):1395 .

3. Sánchez-Moreno L, Perez-Peña A, Duran-Lopez L, et al. Ensemble-based convolutional neural networks for brain tumor classification in MRI: enhancing accuracy and interpretability using explainable AI. Comput Biol Med. 2025;195:110555 .

4. Li C, Zhang F, Du Y, Li H. Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization. Sci Rep. 2024;14(1):15057 .

5. Reyes D, Sánchez J. Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging. Heliyon. 2024;10(3):e110555 .

6. Ganesh S, Gomathi R, Kannadhasan S. Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16. Cancer Biomark. 2025;42(3):18758592241311184 .

  7. Saranya M, Praveena R. Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture. Sci Rep. 2025;15:25286 . 8. Chanemougavel V, Jayanthi K. HAIBTC-TLMO: hybrid artificial intelligence-based brain tumor classification using transfer learning and marine predators optimization. Int J Elect Comput Eng. 2025;12(7):13–23 .

9.Nancy AM, Maheswari R. Brain tumor segmentation and classification using transfer learning based CNN model with model agnostic concept interpretation. Multimedia Tools Appl. 2025;84(5):2509–2538 .

10. Shedbalkar J, Prabhushetty K. Deep transfer learning model for brain tumor segmentation and classification using UNet and chopped VGGNet. Indones J Electr Eng Comput Sci. 2024;33:1405–1415 .

11. Gayathri T, Kumar S. Brain tumor segmentation and classification using CNN pre-trained VGG-16 model in MRI images. IIUM Eng J. 2024;25(2):196–211 .

12. Pokhriyal N, Jain A. Brain tumor classification through MRIs using transfer learning with VGG16 model. Res Appl Sci Eng Technol. 2025;13(1):1821–1827 .

13. Zhao L, Zhang Z. An improved pooling method for convolutional neural networks. Sci Rep. 2024;14(1):1589 .

14. Hyun J, Seong H, Kim E. Universal pooling–a new pooling method for convolutional neural networks. Exp Syst Appl. 2021;180:115084 .

15. Williams T, Li R. Wavelet pooling for convolutional neural networks. In: ICLR; 2018 Feb 15 .

16. Awsaf A. BraTS 2020 Dataset (Training + Validation). Kaggle [Internet]. Available from: https://www.kaggle.com/datasets/awsaf49/brats20-datasettraining-validation .

17. Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):19932024 . https://pubmed.ncbi.nlm.nih.gov/25494501/

18. Bakas S, Reyes M, Jakab A, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629. 2018. Available from: https://www.med.upenn.edu/sbia/brats2020.html

19. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 .

20. Zahid U, Ashraf I, Khan MA, et al. BrainNet: optimal deep learning feature fusion for brain tumor classification. Comput Intel Neurosci. 2022;2022:1–13 .

21. Kalaiselvi T, Padmapriya T, Sriramakrishnan P, Priyadharshini V. Development of automatic glioma brain tumor detection system using deep CNNs. Int J Imaging Syst Technol. 2020;30(4):926–938 .

22.   .Shahzadi I, Tang TB, Meriadeau F, Quyyum A. CNN-LSTM: cascaded framework for brain tumour classification. In: IEEE-EMBS Conf Biomed Eng Sci; 2018; Sarawak, Malaysia. IEEE; 2018. p. 633–637 .

23. Wang X, Yang S, Wu X. Automatic glioma grading based on two-stage networks by integrating pathology and MRI images. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 455–464 .

24. Yin B, Cheng H, Wang F, Wang Z. Brain tumor classification based on MRI images and noise reduced pathology images. In: MICCAI Brainlesion Workshop; . 2020 Oct 4; Cham: Springer. p. 465–474 .

25.  Lerousseau M, Deutsch E, Paragios N. Multimodal brain tumor classification. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 475486 .

26. Pei L, Hsu WW, Chiang LA, et al. A hybrid convolutional neural network method for brain tumor classification using MRI and WSI. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 487–496 .

27. Zhao B, Huang J, Liang C, et al. CNN-based fully automatic glioma classification with multi-modal medical images. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 497–507 .

28. Hamidinekoo A, Pieciak T, Afzali M, et al. Glioma classification using multimodal radiology and histology data. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 508–518 .

29. Liu X, Yao C, Chen H, et al. BTSC‑TNAS: transformer for brain tumor segmentation and classification. Comput Med Imaging Graph. 2023;110:102307 .

30. Aloraini M, Khan A, Aladhadh S, et al. Combining transformer and convolution for brain tumor classification. Appl Sci. 2023;13(6):3680 .

31. Rahman MA, Masum MI, Hasib KM, et al. GliomaCNN: lightweight CNN for brain tumor classification using explainable AI. Comput Model Eng Sci. 2024 .

32. Asad R, Rehman S, Imran A, et al. Early melanoma brain tumor detection using AI. Biomedicines. 2023;11(1):184.

Keywords
Magnetic resonance image processing
tumor
VGG deep neural network
classification
BraTS2020 dataset
Deep learning
Subjects
 1. Louis DN, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncol. 2021;23(8):1231–1251 .
2. Zahoor MM, Khan SH, Alahmadi TJ, et al. Brain tumor MRI classification using a novel deep residual and regional CNN. Biomedicines. 2024;12(7):1395 .
3. Sánchez-Moreno L, Perez-Peña A, Duran-Lopez L, et al. Ensemble-based convolutional neural networks for brain tumor classification in MRI: enhancing accuracy and interpretability using explainable AI. Comput Biol Med. 2025;195:110555 .
4. Li C, Zhang F, Du Y, Li H. Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization. Sci Rep. 2024;14(1):15057 .
5. Reyes D, Sánchez J. Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging. Heliyon. 2024;10(3):e110555 .
6. Ganesh S, Gomathi R, Kannadhasan S. Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16. Cancer Biomark. 2025;42(3):18758592241311184 .
  7. Saranya M, Praveena R. Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture. Sci Rep. 2025;15:25286 . 8. Chanemougavel V, Jayanthi K. HAIBTC-TLMO: hybrid artificial intelligence-based brain tumor classification using transfer learning and marine predators optimization. Int J Elect Comput Eng. 2025;12(7):13–23 .
9.Nancy AM, Maheswari R. Brain tumor segmentation and classification using transfer learning based CNN model with model agnostic concept interpretation. Multimedia Tools Appl. 2025;84(5):2509–2538 .
10. Shedbalkar J, Prabhushetty K. Deep transfer learning model for brain tumor segmentation and classification using UNet and chopped VGGNet. Indones J Electr Eng Comput Sci. 2024;33:1405–1415 .
11. Gayathri T, Kumar S. Brain tumor segmentation and classification using CNN pre-trained VGG-16 model in MRI images. IIUM Eng J. 2024;25(2):196–211 .
12. Pokhriyal N, Jain A. Brain tumor classification through MRIs using transfer learning with VGG16 model. Res Appl Sci Eng Technol. 2025;13(1):1821–1827 .
13. Zhao L, Zhang Z. An improved pooling method for convolutional neural networks. Sci Rep. 2024;14(1):1589 .
14. Hyun J, Seong H, Kim E. Universal pooling–a new pooling method for convolutional neural networks. Exp Syst Appl. 2021;180:115084 .
15. Williams T, Li R. Wavelet pooling for convolutional neural networks. In: ICLR; 2018 Feb 15 .
16. Awsaf A. BraTS 2020 Dataset (Training + Validation). Kaggle [Internet]. Available from: https://www.kaggle.com/datasets/awsaf49/brats20-datasettraining-validation .
17. Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):19932024 . https://pubmed.ncbi.nlm.nih.gov/25494501/
18. Bakas S, Reyes M, Jakab A, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629. 2018. Available from: https://www.med.upenn.edu/sbia/brats2020.html
19. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 .
20. Zahid U, Ashraf I, Khan MA, et al. BrainNet: optimal deep learning feature fusion for brain tumor classification. Comput Intel Neurosci. 2022;2022:1–13 .
21. Kalaiselvi T, Padmapriya T, Sriramakrishnan P, Priyadharshini V. Development of automatic glioma brain tumor detection system using deep CNNs. Int J Imaging Syst Technol. 2020;30(4):926–938 .
22.   .Shahzadi I, Tang TB, Meriadeau F, Quyyum A. CNN-LSTM: cascaded framework for brain tumour classification. In: IEEE-EMBS Conf Biomed Eng Sci; 2018; Sarawak, Malaysia. IEEE; 2018. p. 633–637 .
23. Wang X, Yang S, Wu X. Automatic glioma grading based on two-stage networks by integrating pathology and MRI images. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 455–464 .
24. Yin B, Cheng H, Wang F, Wang Z. Brain tumor classification based on MRI images and noise reduced pathology images. In: MICCAI Brainlesion Workshop; . 2020 Oct 4; Cham: Springer. p. 465–474 .
25.  Lerousseau M, Deutsch E, Paragios N. Multimodal brain tumor classification. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 475486 .
26. Pei L, Hsu WW, Chiang LA, et al. A hybrid convolutional neural network method for brain tumor classification using MRI and WSI. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 487–496 .
27. Zhao B, Huang J, Liang C, et al. CNN-based fully automatic glioma classification with multi-modal medical images. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 497–507 .
28. Hamidinekoo A, Pieciak T, Afzali M, et al. Glioma classification using multimodal radiology and histology data. In: MICCAI Brainlesion Workshop; 2020 Oct 4; Cham: Springer. p. 508–518 .
29. Liu X, Yao C, Chen H, et al. BTSC‑TNAS: transformer for brain tumor segmentation and classification. Comput Med Imaging Graph. 2023;110:102307 .
30. Aloraini M, Khan A, Aladhadh S, et al. Combining transformer and convolution for brain tumor classification. Appl Sci. 2023;13(6):3680 .
31. Rahman MA, Masum MI, Hasib KM, et al. GliomaCNN: lightweight CNN for brain tumor classification using explainable AI. Comput Model Eng Sci. 2024 .
32. Asad R, Rehman S, Imran A, et al. Early melanoma brain tumor detection using AI. Biomedicines. 2023;11(1):184.