ArticleScientific reports2021
Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 1 of them a synthesis that pooled it.
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115 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions.Neurosurgical review · 2026Pooled it
- A hybrid deep learning framework based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images.Magnetic resonance letters · 2026Article
- Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.Quantitative imaging in medicine and surgery · 2026Review
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Glioma Segmentation on Multicenter 2D-Based Magnetic Resonance Imaging Using Low-Rank Adaptation Tuning of a Foundation Model: An External Test Evaluation.Journal of imaging informatics in medicine · 2026Article
- MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms.Journal of imaging informatics in medicine · 2026Article
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- Prediction of aero engine remaining service life using a bidirectional GRU model with self attention mechanism.Scientific reports · 2026Article
- Developing a segmentation cascade deep learning network based on automated prompts.Quantitative imaging in medicine and surgery · 2026Article
- [Research progress of multimodal magnetic resonance imaging brain tumor segmentation based on fused neural network model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- DARE-FUSE: domain aligned evidence guided learning for joint brain tumor MRI segmentation and classification.NPJ digital medicine · 2026Article
- Brain tumor classification from MRI images using a multi-scale channel attention CNN integrated with SVM.Scientific reports · 2026Article
- Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables.Frontiers in neuroscience · 2026Article
- A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation.PloS one · 2026Article
- A Deep Learning Radiomics Model based on Superb Microvascular Imaging for Non-Invasive Prediction of the Degree of Arteriolosclerosis in Patients With Chronic Kidney Disease.Current medical imaging · 2026Article
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- Real-world federated learning for brain imaging scientists.Frontiers in digital health · 2026Article
- A novel sub-differentiable hausdorff loss combined with BCE for MRI brain tumor segmentation using UNet variants.Scientific reports · 2025Article
- Accuracy of intra-operative brain tumor squash cytology compared to large excision biopsy: a comparative study at the University Teaching Hospital of Kigali (CHUK), Rwanda.Journal of neuro-oncology · 2025Article
- Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment.Cancers · 2025Review
55 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.
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