journal article·2024Open access

ResMHA-Net: Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

Novsheena Rasool, Javaid Iqbal Bhat, Najib Ben Aoun, Abdullah Alharthi, Niyaz Ahmad Wani, Vikram Chopra, Muhammad Shahid Anwar

Computers, Materials & Continua· Volume 81· Issue 1

Abstract

Gliomas are aggressive brain tumors known for their heterogeneity, unclear borders, and diverse locations on Magnetic Resonance Imaging (MRI) scans. These factors present significant challenges for MRI-based segmentation, a c... | 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 · Radiomics and Machine Learning in Medical Imaging · Advanced Neural Network Applications

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References (46)

Show source-provided references
1. Exploring the role of neuroplasticity in development, aging, and neurodegeneration
2. TransresuNet: Revolutionizing glioma brain tumor segmentation through transformer-enhanced residual UNet10.1109/ACCESS.2024.3402947 ↗
3. Brain cancers in genetic syndromes
4. Unveiling the complexity of medical imaging through deep learning approaches10.51537/chaos.1326790 ↗
5. Enhancing brain tumor detection in MRI images through explainable ai using grad-cam with ResNet 50
6. Multimodal brain tumor segmentation using 3D-U-Net
7. Brain tumor semi-automatic segmentation on MRI T1-weighted images using active contour models
8. Unveiling the intricacies: A comprehensive review of magnetic resonance imaging (MRI) assessment of T2-weighted hyperintensities in the neuroimaging landscape
9. Overall survival prediction in glioblastoma with radiomic features using machine learning10.3389/fncom.2020.00061 ↗
10. Brain tumour detection using machine and deep learning: A systematic review
11. Deep learning-based cancer detection-recent developments, trend and challenges
12. Exploring task structure for brain tumor segmentation from multi-modality MR images10.1109/TIP.2020.3023609 ↗
13. Multi-level parallel network for brain tumor segmentation
14. Explainable AI-driven IoMT fusion: Unravelling techniques, opportunities, and challenges with explainable AI in healthcare
15. Deepmedic for brain tumor segmentation
16. Automated brain tumor segmentation on multi-modal MR image using SegNet10.1007/s41095-019-0139-y ↗
17. RescueNet: An unpaired GAN for brain tumor segmentation10.1016/j.bspc.2019.101641 ↗
18. State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images
19. Brain image segmentation based on firefly algorithm combined with k-means clustering
20. Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information
21. Glioma brain tumor segmentation using deep learning: A review
22. Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning10.3389/fnins.2019.00810 ↗
23. Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI10.1016/j.inffus.2022.10.022 ↗
24. RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames10.1016/j.compbiomed.2022.106426 ↗
25. MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images10.1016/j.bspc.2022.104296 ↗
26. CB-D2RNet—An efficient context bridge network for glioma segmentation10.1016/j.jvcir.2023.103836 ↗
27. AGGN: Attention-based glioma grading network with multi-scale feature extraction and multi-modal information fusion10.1016/j.compbiomed.2022.106457 ↗
28. Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network10.21037/tcr-23-1858 ↗
29. Clinical capability of modern brain tumor segmentation models10.1002/mp.16321 ↗
30. Ensemble technique for brain tumour patient survival prediction10.1109/ACCESS.2024.3360086 ↗
31. Deep learning-based survival prediction of brain tumor patients using attention-guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging10.1002/ima.23010 ↗
32. Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor10.1016/j.eswa.2023.122347 ↗
33. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks
34. Asymmetric ensemble of asymmetric U-Net models for brain tumor segmentation with uncertainty estimation10.3389/fneur.2021.609646 ↗
35. Glioma segmentation using ensemble of 2D/3D U-Nets and survival prediction using multiple features fusion
36. SGEResU-Net for brain tumor segmentation10.3934/mbe.2022261 ↗
37. Evaluating the impact of intensity normalization on MR image synthesis
38. ME-Net: Multi-encoder net framework for brain tumor segmentation10.1002/ima.22571 ↗
39. An improved 3D U-Net-based deep learning system for brain tumor segmentation using multi-modal MRI
40. HTTU-Net: Hybrid two track U-Net for automatic brain tumor segmentation10.1109/ACCESS.2020.2998601 ↗
41. A computation-efficient CNN system for high-quality brain tumor segmentation10.1016/j.bspc.2021.103475 ↗
42. CorrDiff: Corrective diffusion model for accurate MRI brain tumor segmentation10.1109/JBHI.2024.3353272 ↗
43. Brain tumor segmentation and survival prediction using 3D attention UNet
44. Evolutionary convolutional neural network for efficient brain tumor segmentation and overall survival prediction10.1016/j.eswa.2022.118996 ↗
45. Prediction of survival of glioblastoma patients using local spatial relationships and global structure awareness in flair MRI brain images
46. Computational radiomics system to decode the radiographic phenotype10.1158/0008-5472.CAN-17-0339 ↗