Version 1
: Received: 19 September 2024 / Approved: 19 September 2024 / Online: 19 September 2024 (13:53:32 CEST)
How to cite:
Horng, M.-H.; Yang, T.-H.; Sun, Y.-N.; Li, R.-S. The Detection and Classification of Scaphoid Fracture in Radiograph by Using the Convolutional Neural Network. Preprints2024, 2024091539. https://doi.org/10.20944/preprints202409.1539.v1
Horng, M.-H.; Yang, T.-H.; Sun, Y.-N.; Li, R.-S. The Detection and Classification of Scaphoid Fracture in Radiograph by Using the Convolutional Neural Network. Preprints 2024, 2024091539. https://doi.org/10.20944/preprints202409.1539.v1
Horng, M.-H.; Yang, T.-H.; Sun, Y.-N.; Li, R.-S. The Detection and Classification of Scaphoid Fracture in Radiograph by Using the Convolutional Neural Network. Preprints2024, 2024091539. https://doi.org/10.20944/preprints202409.1539.v1
APA Style
Horng, M. H., Yang, T. H., Sun, Y. N., & Li, R. S. (2024). The Detection and Classification of Scaphoid Fracture in Radiograph by Using the Convolutional Neural Network. Preprints. https://doi.org/10.20944/preprints202409.1539.v1
Chicago/Turabian Style
Horng, M., Yung-Nien Sun and Rong-Shiang Li. 2024 "The Detection and Classification of Scaphoid Fracture in Radiograph by Using the Convolutional Neural Network" Preprints. https://doi.org/10.20944/preprints202409.1539.v1
Abstract
Objective:
Scaphoid fractures, particularly occult and non-displaced fractures, are difficult to detect using traditional X-ray methods due to their subtle appearance and variability in bone density.
The study proposes a two-stage CNN approach to detect and classify scaphoid fractures using anterior-posterior (AP) and lateral (LA) X-ray views for more accurate diagnosis.
Methods:
The study emphasizes the use of multi-view X-ray images (AP and LA views) to improve fracture detection and classification. The multi-view fusion module helps integrate information from both views to enhance detection accuracy, particularly for occult fractures that may not be visible in a single view. The proposed method includes two stages that are stage 1: Detect the scaphoid bone using Faster R-CNN and Feature Pyramid Network (FPN) for region proposal and small object detection. The detection accuracy for scaphoid localization is 100% with Intersection over Union (IoU) scores of 0.8662 for AP views and 0.8478 for LA views. And stage 2: Perform fracture classification using a ResNet backbone and FPN combined with a multi-view fusion module to combine features from both AP and LA views. This stage achieves a classification accuracy of 89.94%, recall of 87.33%, and precision of 90.36%.
Results:
The proposed model performs well in both scaphoid bone detection and fracture classification. The multi-view fusion approach significantly improves recall and accuracy in detecting fractures compared to single-view approaches.
In scaphoid detection, both AP and LA views achieved 100% detection accuracy.
In fracture detection, using multi-view fusion, the accuracy for AP views reached 87.16%, and for LA views, it reached 83.83%.
Conclusion:
The multi-view fusion model effectively improves the detection of scaphoid fractures, particularly in cases of occult and non-displaced fractures.
The model provides a reliable, automated approach to assist clinicians in detecting and diagnosing scaphoid fractures more efficiently.
Keywords
medical image; scaphoid bone; scaphoid fractures,; multi-view detection and segmantation; convolutional neural network
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
