ArticleDiagnostics (Basel, Switzerland)2023
Enhancing X-ray-Based Wrist Fracture Diagnosis Using HyperColumn-Convolutional Block Attention Module.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- FracDet-v11: a multi-scale attention and wavelet-enhanced network for real-time pediatric wrist fracture detection.Scientific reports · 2026Article
- A transfer learning-based approach for automated bone fracture classification in X-ray imaging.Therapeutic advances in musculoskeletal disease · 2026Article
- Optimizing the power of AI for fracture detection: from blind spots to breakthroughs.Skeletal radiology · 2025Review
- Exploring the impact of hyperparameter and data augmentation in YOLO V10 for accurate bone fracture detection from X-ray images.Scientific reports · 2025Article
- Automated machine learning for image-based detection of dental plaque on permanent teeth.Frontiers in dental medicine · 2024Article
- A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
Fractures affect nearly 9.45% of the South Korean population, with radiography being the primary diagnostic tool. This research employs a machine-learning methodology that integrates HyperColumn techniques with the convolutional block attention module (CBAM) to enhance fracture detection in X-ray radiographs. Utilizing the EfficientNet-B0 and DenseNet169 models bolstered by the HyperColumn and the CBAM, distinct improvements in fracture site prediction emerge. Significantly, when HyperColumn and CBAM integration is applied, both DenseNet169 and EfficientNet-B0 showed noteworthy accuracy improvements, with increases of approximately 0.69% and 0.70%, respectively. The HyperColumn-CBAM-DenseNet169 model particularly stood out, registering an uplift in the AUC score from 0.8778 to 0.9145. The incorporation of Grad-CAM technology refined the heatmap's focus, achieving alignment with expert-recognized fracture sites and alleviating the deep-learning challenge of heavy reliance on bounding box annotations. This innovative approach signifies potential strides in streamlining training processes and augmenting diagnostic precision in fracture detection.
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