A Global-Local Parallel Dual-Branch Deep Learning Model with Attention-Enhanced Feature Fusion for Brain Tumor MRI Classification
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Abstract
Brain tumor classification is crucial for personalized treatment planning. Although deep learning-based Artificial Intelligence (AI) models can automatically analyze tumor images, fine details of small tumor regions may be ov... | Find, read and cite all the research you need on Tech Science Press
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OpenAlex topics: Brain Tumor Detection and Classification
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References (42)
Show source-provided references
1. Primary brain tumours in adults10.1016/S0140-6736(18)30990-5 ↗
2. Pichaivel · Brain tumors. · 2022 [cited 2024 Nov 23]
3. Overcoming the blood-brain barrier for the therapy of malignant brain tumor: current status and prospects of drug delivery approaches10.1186/s12951-022-01610-7 ↗
4. Molecular pathology of tumors of the central nervous system10.1093/annonc/mdz164 ↗
5. Differential deep convolutional neural network model for brain tumor classification10.3390/brainsci11030352 ↗
6. A decision support system for multimodal brain tumor classification using deep learning
7. Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm10.1002/ima.22831 ↗
8. The present and future of deep learning in radiology10.1016/j.ejrad.2019.02.038 ↗
9. Machine learning applications to neuroimaging for glioma detection and classification: an artificial intelligence augmented systematic review10.1016/j.jocn.2021.04.043 ↗
10. A test statistic estimation-based approach for establishing self-interpretable CNN-based binary classifiers10.1109/TMI.2023.3348699 ↗
11. Leveraging efficientNetB3 in a deep learning framework for high-accuracy MRI tumor classification
12. Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI10.1016/j.inffus.2022.10.022 ↗
13. Sparse dynamic volume transUNet with multi-level edge fusion for brain tumor segmentation10.1016/j.compbiomed.2024.108284 ↗
14. Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor10.1016/j.eswa.2023.122347 ↗
15. Hybrid archimedes sine cosine optimization enabled deep learning for multilevel brain tumor classification using mri images10.1016/j.bspc.2023.105419 ↗
16. Graph attention autoencoder inspired CNN based brain tumor classification using MRI10.1016/j.neucom.2022.06.107 ↗
17. Brainnet: precision brain tumor classification with optimized efficientnet architecture10.1155/2024/3583612 ↗
18. Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data10.1016/j.bspc.2022.104424 ↗
19. BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification10.1038/s41598-024-56657-3 ↗
20. Brain tumor classification using weighted least square twin support vector machine with fuzzy hyperplane10.1016/j.engappai.2024.109450 ↗
21. Tumor localization and classification from MRI of brain using deep convolution neural network and Salp swarm algorithm10.1007/s12559-022-10096-2 ↗
22. Brain-GCN-Net: graph-convolutional neural network for brain tumor identification10.1016/j.compbiomed.2024.108971 ↗
23. Enhanced brain tumor classification using graph convolutional neural network architecture10.1038/s41598-023-41407-8 ↗
24. A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images10.1007/s13042-024-02110-w ↗
25. Cheng J. Brain tumor dataset [Online]. Figshare; 2017 [cited 2024 Nov 23]. Available: http://dx.doi.org/10.6084/m9.figshare.1512427.v5.
26. Deep residual learning for image recognition
27. Exploring self-attention for image recognition
28. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv:201011929. 2020.
29. Attention is all you need
30. Bottleneck transformers for visual recognition
31. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:14091556. 2014.
32. ECA-Net: efficient channel attention for deep convolutional neural networks
33. Mao A, Mohri M, Zhong Y. Cross-entropy loss functions: theoretical analysis and applications. 2023. doi:10.48550/arXiv.2304.07288.
34. Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion10.1016/j.neucom.2022.11.039 ↗
35. WBM-DLNets: wrapper-based metaheuristic deep learning networks feature optimization for enhancing brain tumor detection10.3390/bioengineering10040475 ↗
36. Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning10.1016/j.bbe.2023.08.003 ↗
37. Automated brain tumor diagnosis using deep residual u-net segmentation model
38. Montoya · Classification of brain tumors: a comparative approach of shallow and deep neural networks · 2024 · 5 · 142
39. MEHW-SVM multi-kernel approach for improved brain tumour classification10.1049/ipr2.12990 ↗
40. An integrated convolutional neural network with attention guidance for improved performance of medical image classification10.1007/s00521-023-09164-x ↗
41. Deep learning and optimized learning machine for brain tumor classification10.1016/j.bspc.2023.105778 ↗
42. Hybrid-NET: a fusion of DenseNet169 and advanced machine learning classifiers for enhanced brain tumor diagnosis10.1002/ima.22975 ↗