Enhancing Wrist Fracture Detection with YOLO

• URL: http://arxiv.org/abs/2407.12597v2
• Date: Mon, 29 Jul 2024 23:26:11 GMT
• Title: Enhancing Wrist Fracture Detection with YOLO
• Authors: Ammar Ahmed, Ali Shariq Imran, Abdul Manaf, Zenun Kastrati, Sher Muhammad Daudpota,
• Abstract summary: This study employs state-of-the-art single-stage deep neural network-based detection models YOLOv5, YOLOv6, YOLOv7, and YOLOv8 to detect wrist abnormalities. We found that these YOLO models outperform the commonly used two-stage detection algorithm, Faster R-CNN, in fracture detection.
• Score: 3.2049746597433746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 YOLOv8m demonstrating a highest fracture detection sensitivity of 0.92 and mean average precision (mAP) of 0.95. On the other hand, YOLOv6m 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.

Related papers

• YOLOv8-ResCBAM: YOLOv8 Based on An Effective Attention Module for Pediatric Wrist Fracture Detection [0.0]
  This paper proposes YOLOv8-ResCBAM, which incorporates Convolutional Block Attention Module integrated with resblock (ResCBAM) into the original YOLOv8 network architecture. The experimental results on the GRAZPEDWRI-DX dataset demonstrate that the mean Average Precision calculated at Intersection over Union threshold of 0.5 (mAP 50) of the proposed model increased from 63.6% to 65.8%.
  arXiv  Detail & Related papers  (2024-09-27T15:19:51Z)
• Pediatric Wrist Fracture Detection in X-rays via YOLOv10 Algorithm and Dual Label Assignment System [2.4554686192257424]
  This study is the first to evaluate various YOLOv10 variants to assess their performance in detecting pediatric wrist fractures. It investigates how changes in model complexity, scaling the architecture, and implementing a dual-label assignment strategy can enhance detection performance.
  arXiv  Detail & Related papers  (2024-07-22T14:54:51Z)
• Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection [0.0]
  Children often suffer wrist injuries in daily life, while fracture injuring radiologists need to analyze and interpret X-ray images before surgical treatment. The development of deep learning has enabled neural network models to work as computer-assisted diagnosis (CAD) tools. This paper proposes the YOLOv8 model for fracture detection, which is an improved version of the YOLOv8 model with the GC block.
  arXiv  Detail & Related papers  (2024-07-03T14:36:07Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging [77.32032399775152]
  Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability.
  arXiv  Detail & Related papers  (2022-01-27T16:37:19Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks [0.4697611383288171]
  Deep convolutional neural networks (D-CNNs) have shown remarkable performance in interpreting chest radiograph (CXR) scans in adults. In this paper, we retrospectively collect a large dataset of 5,017 pediatric CXR scans, for which each is manually labeled by an experienced radiologist. A D-CNN model is then trained on 3,550 annotated scans to classify multiple pediatric lung pathologies automatically.
  arXiv  Detail & Related papers  (2021-08-14T08:14:52Z)
• Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques [111.165389441988]
  This work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI) Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
  arXiv  Detail & Related papers  (2021-05-18T11:37:57Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection [1.0617212070722408]
  Wrist Fracture is the most common type of fracture with a high incidence rate. Recent advances in the field of Deep Learning (DL) have shown that wrist fracture detection can be automated using Convolutional Neural Networks. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, has a substantially lower performance on the challenging test set.
  arXiv  Detail & Related papers  (2020-12-04T13:35:36Z)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.