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Enhancing wrist abnormality detection with YOLO : Analysis of state-of-the-art single-stage detection models

Ahmed, Ammar (författare)
Sukkur IBA University, Pakistan
Imran, Ali Shariq (författare)
Norwegian University of Science and Technology, Norway
Manaf, Abdul (författare)
Sukkur IBA University, Pakistan
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Kastrati, Zenun, 1984- (författare)
Linnéuniversitetet,Institutionen för informatik (IK)
Daudpota, Sher Muhammad (författare)
Sukkur IBA University, Pakistan
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 (creator_code:org_t)
Elsevier, 2024
2024
Engelska.
Ingår i: Biomedical Signal Processing and Control. - : Elsevier. - 1746-8094 .- 1746-8108. ; 93
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Diagnosing and treating abnormalities in the wrist, specifically distal radius, and ulna fractures, is a crucial concern among children, adolescents, and young adults, with a higher incidence rate during puberty. However, the scarcity of radiologists and the lack of specialized training among medical professionals pose a significant risk to patient care. This problem is further exacerbated by the rising number of imaging studies and limited access to specialist reporting in certain regions. This highlights the need for innovative solutions to improve the diagnosis and treatment of wrist abnormalities. Automated wrist fracture detection using object detection has shown potential, but current studies mainly use two-stage detection methods with limited evidence for single-stage effectiveness. This study employs state-of-the-art single-stage deep neural network-based detection models YOLOv5, YOLOv6, YOLOv7, and YOLOv8 to detect wrist abnormalities. Through extensive experimentation, we found that these YOLO models outperform the commonly used two-stage detection algorithm, Faster R-CNN, in fracture detection. Additionally, compound-scaled variants of each YOLO model were compared, with YOLOv8 m demonstrating a highest fracture detection sensitivity of 0.92 and mean average precision (mAP) of 0.95. On the other hand, YOLOv6 m achieved the highest sensitivity across all classes at 0.83. Meanwhile, YOLOv8x recorded the highest mAP of 0.77 for all classes on the GRAZPEDWRI-DX pediatric wrist dataset, highlighting the potential of single-stage models for enhancing pediatric wrist imaging.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Nyckelord

Wrist fracture detection
Object localization
Medical imaging
Pediatric X-ray
Deep learning
YOLO
Informations- och programvisualisering
Information and software visualization

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