Current - Issue
Original Article
A CNN-Transformer Hybrid Framework for Automated MRI Image-Based Brain Tumor Classification
D. Bala Chandu1
T. Purna sai2
Kiran kumar kalagadda3
1 2 Department of Computer Science & Engineering Vignan’s Foundation for Science, Technology and Research Vadlamudi, Guntur, Andhra Pradesh., India. 3 Assistant Professor, Department of Computer Science & Engineering Vignan’s Foundation for Science, Technology and Research Guntur, Andhra Pradesh, India.
Published Online: May-August 2026
Pages: 241-246
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502027
References
1) G. Litjens et al., “A survey on deep learning in medical image analysis,” Med. Image Anal., vol. 42, pp. 60–88, 2017.
2) H. H. Sultan, N. M. Salem, and W. Al-Atabany, “Multi-classification of brain tumor images using deep neural network,” IEEE Access,
vol. 7, pp. 69881–69891, 2019.
3) A. Dosovitskiy et al., “An image is worth 16×16 words: Transformers for image recognition at scale,” in Proc. ICLR, 2021.
4) Z. Liu et al., “Swin Transformer: Hierarchical vision transformer using shifted windows,” in Proc. IEEE/CVF ICCV, pp. 10012–10022,
2021.
5) H. Cao et al., “Swin-Unet: Unet-like pure transformer for medical image segmentation,” in Proc. ECCV Workshops, 2022.
6) S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmenta- tion using convolutional neural networks in MRI images,” IEEE
Trans. Med. Imag., vol. 35, no. 5, pp. 1240–1251, 2016.
7) Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Comput. Biol. Med., vol. 111, p.
103345, 2019.
8) N. Ghassemi, A. Shoeibi, and M. Rouhani, “Deep neural network with generative adversarial networks pre-training for brain tumor classifica-
tion,” Biomed. Signal Process. Control, vol. 57, p. 101678, 2020.
9) M. Sajjad et al., “Multi-grade brain tumor classification using deep CNN with extensive data augmentation,” J. Comput. Sci., vol. 30, pp. 174–
182, 2019.
10) K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE CVPR, pp. 770–778, 2016.
11) D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. ICLR, 2015.
12) S.Bhuvaji et al., “Brain Tumor Classification (MRI),” Kaggle Dataset, 2020. [Online]. Available:
https://www.kaggle.com/sartajbhuvaji/brain- tumor-classification-mri
2) H. H. Sultan, N. M. Salem, and W. Al-Atabany, “Multi-classification of brain tumor images using deep neural network,” IEEE Access,
vol. 7, pp. 69881–69891, 2019.
3) A. Dosovitskiy et al., “An image is worth 16×16 words: Transformers for image recognition at scale,” in Proc. ICLR, 2021.
4) Z. Liu et al., “Swin Transformer: Hierarchical vision transformer using shifted windows,” in Proc. IEEE/CVF ICCV, pp. 10012–10022,
2021.
5) H. Cao et al., “Swin-Unet: Unet-like pure transformer for medical image segmentation,” in Proc. ECCV Workshops, 2022.
6) S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmenta- tion using convolutional neural networks in MRI images,” IEEE
Trans. Med. Imag., vol. 35, no. 5, pp. 1240–1251, 2016.
7) Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Comput. Biol. Med., vol. 111, p.
103345, 2019.
8) N. Ghassemi, A. Shoeibi, and M. Rouhani, “Deep neural network with generative adversarial networks pre-training for brain tumor classifica-
tion,” Biomed. Signal Process. Control, vol. 57, p. 101678, 2020.
9) M. Sajjad et al., “Multi-grade brain tumor classification using deep CNN with extensive data augmentation,” J. Comput. Sci., vol. 30, pp. 174–
182, 2019.
10) K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE CVPR, pp. 770–778, 2016.
11) D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. ICLR, 2015.
12) S.Bhuvaji et al., “Brain Tumor Classification (MRI),” Kaggle Dataset, 2020. [Online]. Available:
https://www.kaggle.com/sartajbhuvaji/brain- tumor-classification-mri
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