ArticleBMC medical imaging2024
Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Hybrid Approach for Brain-Tumor Detection and Classification from MRI Images.Journal of imaging · 2026Article
- Predicting local control of brain metastases after Gamma Knife radiosurgery: a multimodal deep learning-radiomics approach.BMC medical imaging · 2026Article
- Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging.Scientific reports · 2026Article
- A contrastive learning framework with adaptive feature fusion for brain tumor classification.Scientific reports · 2026Article
- MM FD ConvFormer multimodal frequency aware deformable CNN transformer network for robust brain tumor classification.Scientific reports · 2026Article
- ResSGA-Net: A deep learning approach for enhanced brain tumor detection and accurate classification in healthcare imaging systems.Journal, genetic engineering & biotechnology · 2026Article
- Bridging modalities: a deep learning framework for brain tumor classification via CT-MRI integration and model fusion.Frontiers in computational neuroscience · 2026Article
- Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.Brain informatics · 2025Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.Bioengineering (Basel, Switzerland) · 2025Article
- Radiomics early assessment of post chemotherapy cardiotoxicity in cancer patients using 2D echocardiography imaging an interpretable machine learning study.Scientific reports · 2025Article
- HTRecNet: a deep learning study for efficient and accurate diagnosis of hepatocellular carcinoma and cholangiocarcinoma.Frontiers in cell and developmental biology · 2025Article
- Efficient brain tumor grade classification using ensemble deep learning models.BMC medical imaging · 2024Article
- Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques.Scientific reports · 2024Article
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Authors and funding
6 authors.
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Abstract
Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists' expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians.
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