“An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow”

Title:“An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow”

Volume: 20

Author(s): Taukir Alam, Wei-Cheng Yeh*, Fang Rong Hsu*, Shia Wei-Chung, A. Robert Singh, Taimoor Hassan, Wenru Lin, Hong-Ye Yang and Tahir Hussain

Affiliation:

• Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
• Department of Medical Imaging Chang Bing Show Chwan Memorial Hospital Diagnostic Radiology Specialist, Changhua, Taiwan

• Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan

Keywords: YOLO, ROI, ResNet, SeResNet, Vision transformer, Fracture, X-ray, Medical images.

Abstract:

Introduction: In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.

Methods: Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity.

Results: These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95.

Conclusion: This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

Cite this article as:

Alam Taukir, Yeh Wei-Cheng*, Hsu Fang Rong*, Wei-Chung Shia, Singh A. Robert, Hassan Taimoor, Lin Wenru, Yang Hong-Ye and Hussain Tahir, “An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow”, Current Medical Imaging 2024; 20 : e15734056309890 . https://dx.doi.org/10.2174/0115734056309890240912054616