A Review of Deep Learning Methods for Brain Tumor Detection
DOI:
https://doi.org/10.4108/eetel.8441Keywords:
Deep Learning, Disease Diagnosis, Brain Tumour, Medical ImageAbstract
A brain tumor is a serious neurological condition caused by the growth of abnormal cells in various regions of the brain, leading to a variety of health issues. Although the specific causes of brain tumors are not yet fully understood, known risk factors include genetic predisposition, ionizing radiation, viral infections, and exposure to certain chemicals. With the advancement of deep learning technology, computer-aided diagnosis systems can offer crucial support for the early diagnosis of brain tumors. Brain tumor image classification using deep learning has emerged as a prominent area of research. This article begins by summarizing the publicly available datasets frequently utilized in brain tumor classification tasks. It then provides an overview of the models commonly applied for diagnosing brain tumors. Following this, the paper reviews the advancements made in the field of brain tumor classification research to date. Finally, it highlights the future trends and challenges in brain tumor classification.
References
[1] M. Abed, “A Comprehensive Examination of Human Brain Disorders,” Journal of Biomedical and Sustainable Healthcare Applications, vol. 3, no. 2, pp. 141–152, 2023.
[2] U. Raghavendra et al., “Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives,” Computers in Biology and Medicine, vol. 163, p. 107063, 2023.
[3] D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual review of biomedical engineering, vol. 19, no. 1, pp. 221–248, 2017.
[4] X. Liu and Z. Wang, “Deep learning in medical image classification from mri-based brain tumor images,” in 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), IEEE, 2024, pp. 840–844.
[5] E. Song, B. Zhan, and H. Liu, “Combining external-latent attention for medical image segmentation,” Neural Networks, vol. 170, pp. 468–477, 2024.
[6] Y. Zhou, X. Yang, J. Yin, and S. Liu, “Research on Multi-Scale Feature Fusion Network Algorithm Based on Brain Tumor Medical Image Classification.,” Computers, Materials & Continua, vol. 79, no. 3, 2024.
[7] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
[8] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017.
[9] A. Dosovitskiy, “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
[10] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
[11] J. Cheng, “brain tumor dataset.” figshare, p. 879509079 Bytes, 2017. doi: 10.6084/M9.FIGSHARE.1512427.V5.
[12] M. Aloraini, A. Khan, S. Aladhadh, S. Habib, M. F. Alsharekh, and M. Islam, “Combining the transformer and convolution for effective brain tumor classification using MRI images,” Applied Sciences, vol. 13, no. 6, p. 3680, 2023.
[13] C. Tang, B. Li, J. Sun, S.-H. Wang, and Y.-D. Zhang, “GAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor classification,” Journal of King Saud University-Computer and Information Sciences, vol. 35, no. 2, pp. 560–575, 2023.
[14] M. Nickparvar, “Brain Tumor MRI Dataset.” Kaggle, 2021. doi: 10.34740/KAGGLE/DSV/2645886.
[15] R. Girshick, “Fast R-CNN,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440–1448. doi: 10.1109/ICCV.2015.169.
[16] S. Soni, S. S. Chouhan, and S. S. Rathore, “TextConvoNet: A convolutional neural network based architecture for text classification,” Applied Intelligence, vol. 53, no. 11, pp. 14249–14268, 2023.
[17] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998, doi: 10.1109/5.726791.
[18] S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. Niakan Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Medical Informatics and Decision Making, vol. 23, no. 1, p. 16, 2023.
[19] D.-X. Xue, R. Zhang, H. Feng, and Y.-L. Wang, “CNN-SVM for microvascular morphological type recognition with data augmentation,” Journal of medical and biological engineering, vol. 36, pp. 755–764, 2016.
[20] Z. A. Sejuti and M. S. Islam, “An efficient method to classify brain tumor using CNN and SVM,” in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), IEEE, 2021, pp. 644–648.
[21] B. Srinivas and G. S. Rao, “A hybrid CNN-KNN model for MRI brain tumor classification,” Int. J. Recent Technol. Eng, vol. 8, no. 2, pp. 5230–5235, 2019.
[22] S. Deepak and P. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Computers in biology and medicine, vol. 111, p. 103345, 2019.
[23] C. Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9.
[24] A. M. Al-Zoghby, E. M. K. Al-Awadly, A. Moawad, N. Yehia, and A. I. Ebada, “Dual deep cnn for tumor brain classification,” Diagnostics, vol. 13, no. 12, p. 2050, 2023.
[25] B. Masoudi, “An optimized dual attention-based network for brain tumor classification,” International Journal of System Assurance Engineering and Management, pp. 1–12, 2024.
[26] B. V. Isunuri and J. Kakarla, “Three-class brain tumor classification from magnetic resonance images using separable convolution based neural network,” Concurrency and Computation: Practice and Experience, vol. 34, no. 1, p. e6541, 2022.
[27] T. Dozat, “Incorporating nesterov momentum into adam,” 2016.
[28] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” in Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, Springer, 2018, pp. 270–279.
[29] Y. Yang et al., “Glioma grading on conventional MR images: a deep learning study with transfer learning,” Frontiers in neuroscience, vol. 12, p. 804, 2018.
[30] A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems, 2017.
[31] I. Sutskever, “Sequence to Sequence Learning with Neural Networks,” arXiv preprint arXiv:1409.3215, 2014.
[32] S. Tummala, S. Kadry, S. A. C. Bukhari, and H. T. Rauf, “Classification of brain tumor from magnetic resonance imaging using vision transformers ensembling,” Current Oncology, vol. 29, no. 10, pp. 7498–7511, 2022.
[33] J. Yang, A. Rusak, and A. Belozubov, “Enhancing Brain Tumor Classification Using Data-Efficient Image Transformer,” in 2024 International Russian Smart Industry Conference (SmartIndustryCon), IEEE, 2024, pp. 339–343.
[34] Z. Liu et al., “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10012–10022.
[35] G. J. Ferdous, K. A. Sathi, M. A. Hossain, M. M. Hoque, and M. A. A. Dewan, “LCDEiT: A linear complexity data-efficient image transformer for MRI brain tumor classification,” IEEE Access, vol. 11, pp. 20337–20350, 2023.
[36] M. Aloraini, A. Khan, S. Aladhadh, S. Habib, M. F. Alsharekh, and M. Islam, “Combining the transformer and convolution for effective brain tumor classification using MRI images,” Applied Sciences, vol. 13, no. 6, p. 3680, 2023.
[37] S. Tabatabaei, K. Rezaee, and M. Zhu, “Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system,” Biomedical Signal Processing and Control, vol. 86, p. 105119, 2023.
[38] T. K. Dutta, D. R. Nayak, and R. B. Pachori, “GT-Net: global transformer network for multiclass brain tumor classification using MR images,” Biomedical Engineering Letters, pp. 1–9, 2024.
[39] J. Wang, S.-Y. Lu, S.-H. Wang, and Y.-D. Zhang, “RanMerFormer: Randomized vision transformer with token merging for brain tumor classification,” Neurocomputing, vol. 573, p. 127216, 2024.
[40] V. S. R. Gade, R. K. Cherian, B. Rajarao, and M. A. Kumar, “BMO based improved Lite Swin transformer for brain tumor detection using MRI images,” Biomedical Signal Processing and Control, vol. 92, p. 106091, 2024.
[41] S. He, Z. Li, Y. Tang, Z. Liao, F. Li, and S.-J. Lim, “Parameters compressing in deep learning,” Computers, Materials & Continua, vol. 62, no. 1, pp. 321–336, 2020.
[42] P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017.
[43] B. Chen, J. Li, G. Lu, H. Yu, and D. Zhang, “Label co-occurrence learning with graph convolutional networks for multi-label chest x-ray image classification,” IEEE journal of biomedical and health informatics, vol. 24, no. 8, pp. 2292–2302, 2020.
[44] Y. Liu, F. Zhang, Q. Zhang, S. Wang, Y. Wang, and Y. Yu, “Cross-view correspondence reasoning based on bipartite graph convolutional network for mammogram mass detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3812–3822.
[45] M. Ravinder et al., “Enhanced brain tumor classification using graph convolutional neural network architecture,” Scientific Reports, vol. 13, no. 1, p. 14938, 2023.
[46] E. Gürsoy and Y. Kaya, “Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification,” Computers in Biology and Medicine, vol. 180, p. 108971, 2024.
[47] L. Mishra and S. Verma, “Graph attention autoencoder inspired CNN based brain tumor classification using MRI,” Neurocomputing, vol. 503, pp. 236–247, 2022.
[48] A. Salehi and H. Davulcu, “Graph attention auto-encoders,” arXiv preprint arXiv:1905.10715, 2019.
[49] D. Chen, Y. Lin, W. Li, P. Li, J. Zhou, and X. Sun, “Measuring and relieving the over-smoothing problem for graph neural networks from the topological view,” in Proceedings of the AAAI conference on artificial intelligence, 2020, pp. 3438–3445.
[50] G. Li, C. Xiong, A. Thabet, and B. Ghanem, “Deepergcn: All you need to train deeper gcns,” arXiv preprint arXiv:2006.07739, 2020.
[51] L. Liu, J. Chang, P. Zhang, H. Qiao, and S. Xiong, “SASG-GCN: self-attention similarity guided graph convolutional network for multi-type lower-grade glioma classification,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 7, pp. 3384–3395, 2023.
[52] Y. Rong, W. Huang, T. Xu, and J. Huang, “Dropedge: Towards deep graph convolutional networks on node classification,” arXiv preprint arXiv:1907.10903, 2019.
[53] Z. Tang, Z.-H. Sun, E. Q. Wu, C.-F. Wei, D. Ming, and S.-D. Chen, “MRCG: A MRI Retrieval Framework With Convolutional and Graph Neural Networks for Secure and Private IoMT,” IEEE journal of biomedical and health informatics, vol. 27, no. 2, pp. 814–822, 2021.
[54] S. Liu and R. Gui, “Fusing multi-scale fMRI features using a brain-inspired multi-channel graph neural network for major depressive disorder diagnosis,” Biomedical Signal Processing and Control, vol. 90, p. 105837, 2024.
[55] G. Li, M. Muller, A. Thabet, and B. Ghanem, “Deepgcns: Can gcns go as deep as cnns?,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 9267–9276.
[56] I. Salim and A. B. Hamza, “Classification of developmental and brain disorders via graph convolutional aggregation,” Cognitive Computation, vol. 16, no. 2, pp. 701–716, 2024.
[57] P. Khatiwada, B. Yang, J.-C. Lin, and B. Blobel, “Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy,” Journal of Personalized Medicine, vol. 14, no. 3, p. 282, 2024.
[58] J. Neves et al., “Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning,” European Journal of Radiology, p. 111341, 2024.
[59] P. Esmaeilzadeh, “Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations,” Artificial Intelligence in Medicine, vol. 151, p. 102861, 2024.
[60] S. Pan et al., “Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model,” Medical Physics, vol. 51, no. 4, pp. 2538–2548, 2024.
[61] C. Ge, I. Y.-H. Gu, A. S. Jakola, and J. Yang, “Enlarged training dataset by pairwise GANs for molecular-based brain tumor classification,” IEEE access, vol. 8, pp. 22560–22570, 2020.
[62] M. Yanzhen, C. Song, L. Wanping, Y. Zufang, and A. Wang, “Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review,” Frontiers in Neuroscience, vol. 18, p. 1401329, 2024.
[63] S. Liu, L. Qu, S. Yin, M. Wang, and Z. Song, “Wavelet-based spectrum transfer with collaborative learning for unsupervised bidirectional cross-modality domain adaptation on medical image segmentation,” Neural Computing and Applications, vol. 36, no. 12, pp. 6741–6755, 2024.
[64] X. Chen and Y. Peng, “CMCD-Net: Unsupervised domain adaptation with Contrastive learning for cross-modality and cross-disease brain lesion segmentation,” in 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2024, pp. 4869–4876.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2024 Shuaichao Wen
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
