mlsanityData to 14 Oct 2026

FracDetNet: Advanced Fracture Detection via Dual-Focus Attention and Multi-scale Calibration in Medical X-ray Imaging

arXiv 2509.23416 ↗First posted 27 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

In this paper, an advanced fracture detection framework, FracDetNet, is proposed to address challenges in medical imaging, as accurate fracture detection is essential for enhancing diagnostic efficiency in clinical practice. Despite recent advancements, existing methods still struggle with detecting subtle and morphologically diverse fractures due to variable imaging angles and suboptimal image quality. To overcome these limitations, FracDetNet integrates Dual-Focus Attention (DFA) and Multi-scale Calibration (MC). Specifically, the DFA module effectively captures detailed local features and comprehensive global context through combined global and local attention mechanisms. Additionally, the MC adaptively refines feature representations to enhance detection performance. Experimental evaluations on the publicly available GRAZPEDWRI-DX dataset demonstrate state-of-the-art performance, with FracDetNet achieving a mAP50-95 of 40.0%, reflecting a 7.5% improvement over the baseline model. Furthermore, the mAP50 reaches 63.9%, representing an increase of 4.2%, with fracture-specific detection accuracy also enhanced by 2.9%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 27 Sep 2025.

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References · 20

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8 more
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  3. 2023Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithmcited by 12
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