A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor
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Abstract
Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because ... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Brain Tumor Detection and Classification · Advanced Neural Network Applications · Radiomics and Machine Learning in Medical Imaging
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References (45)
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1. State of the art survey on MRI brain tumor segmentation
2. 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 ↗
3. Diagnostic accuracy of diffusion-weighted imaging in differentiating glioma recurrence from posttreatment-related changes: A meta-analysis
4. MRI-based brain tumor image detection using CNN based deep learning method10.1016/j.neuri.2022.100060 ↗
5. QU-BraTS: MICCAI BraTS, 2020 challenge on quantifying uncertainty in brain tumor segmentation-analysis of ranking scores and benchmarking results10.59275/j.melba.2022-354b ↗
6. Fully convolutional networks for semantic segmentation
7. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks
8. Residual UNet with dual attention—An ensemble residual UNet with dual attention for multi-modal and multi-class brain MRI segmentation
9. Mutual ensemble learning for brain tumor segmentation10.1016/j.neucom.2022.06.058 ↗
10. Medical image segmentation using watershed segmentation with texture-based region merging
11. D2BOF-COVIDNet: A framework of deep bayesian optimization and fusion-assisted optimal deep features for COVID-19 classification using chest x-ray and MRI scans10.3390/diagnostics13010101 ↗
12. BF2SkNet: Best deep learning features fusion-assisted framework for multiclass skin lesion classification
13. Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm
14. A healthcare system for COVID19 classification using multi-type classical features selection
15. Brain MRI analysis using deep neural network for medical of internet things applications10.1016/j.compeleceng.2022.108386 ↗
16. COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization
17. Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation10.1016/j.cviu.2010.09.007 ↗
18. Cellular neural networks assisted automatic detection of elements in microscopic medical images. A preliminary study
19. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
20. Computer-aided classification of lung nodules on computed tomography images via deep learning technique
21. Brain tumor segmentation using convolutional neural networks in MRI images
22. U-net: Convolutional networks for biomedical image segmentation
23. V-Net: Fully convolutional neural networks for volumetric medical image segmentation
24. Deep learning based brain tumor segmentation: A survey
25. Residual attention network for image classification
26. A 2-nets: Double attention networks
27. Dual attention networks for multimodal reasoning and matching
28. Learning a discriminative feature network for semantic segmentation
29. Attention u-net: Learning where to look for the pancreas
30. RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
31. TA-Net: Triple attention network for medical image segmentation10.1016/j.compbiomed.2021.104836 ↗
32. Dense encoder-decoder network based on two-level context enhanced residual attention mechanism for segmentation of breast tumors in magnetic resonance imaging
33. Hybrid dilation and attention residual U-Net for medical image segmentation
34. Squeeze-and-excitation networks
35. The multimodal brain tumor image segmentation benchmark (brats) medical imaging
36. Evaluating intensity normalization on MRIs of human brain with multiple sclerosis
37. Deep learning based brain tumor segmentation: A survey
38. A novel multi-modal MRI synthesis network with enhanced quality and controllability for brain tumor segmentation
39. BRAINS: Boundary-aware attentive network for brain tumor segmentation
40. TA-Net: Triple attention network for medical image segmentation10.1016/j.compbiomed.2021.104836 ↗
41. Hybrid dilation and attention residual U-Net for medical image segmentation10.1016/j.compbiomed.2021.104449 ↗
42. Multilevel volumetric attention network for brain tumor segmentation
43. CBAM-Unet: A novel convolutional block attention module based Unet for accurate brain tumor segmentation
44. FCGAN: Fully convolutional generative adversarial network for brain tumor segmentation
45. Automatic brain tumor segmentation with scale attention network