An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture
Taukir Alam1, Wei-Cheng Yeh1,2, Fang Rong Hsu1
1Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan.
Current Medical Imaging
|October 3, 2024
Summary
This study enhances elbow fracture prediction using AI algorithms like YOLOv8, ResNet, SeResNet, and Vision Transformer on X-ray images. The AI models achieved high accuracy, improving diagnostic precision.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Orthopedics
Background:
- Elbow fractures require accurate and timely diagnosis.
- Current diagnostic methods can be time-consuming and may benefit from AI assistance.
Purpose of the Study:
- To refine elbow fracture prediction using advanced AI algorithms on X-ray images.
- To compare the performance of YOLOv8, ResNet, SeResNet, and Vision Transformer for fracture detection.
Main Methods:
- Utilized YOLOv8 for initial identification of Regions of Interest (ROI) in elbow X-rays.
- Integrated and compared ResNet, SeResNet, and Vision Transformer algorithms for fracture prediction.
- Applied image enhancement techniques and recalibrated ROIs for improved diagnostic accuracy.
Main Results:
- Vision Transformer (ViT) achieved the highest accuracy (0.99), with ResNet and SeResNet achieving 0.97.
- All algorithms demonstrated high precision (1.0) and recall (0.95).
- The combined approach significantly improved fracture prediction accuracy.
Conclusions:
- The developed AI-driven approach shows potential for increasing diagnostic precision in elbow fracture detection.
- This technology can assist radiologists, integrate into existing systems, and support clinical decision-making for better patient care.
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