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

• Published in: Current medical imaging reviews Vol. 20; p. e15734056309890
• Main Authors: Alam, Taukir, Yeh, Wei-Cheng, Hsu, Fang Rong, Shia, Wei-Chung, Singh, Robert, Hassan, Taimoor, Lin, Wenru, Yang, Hong-Ye, Hussain, Tahir
• Format: Journal Article
• Language: English
• Published: United Arab Emirates 01.01.2024
• Subjects: Algorithms; Elbow - diagnostic imaging; Elbow Injuries; Elbow Joint - diagnostic imaging; Fractures, Bone - diagnostic imaging; Humans; Radiography - methods; YOLO; Medical Images; SeResnet; Fracture; Vision Transformer; X-ray; ROI; Resnet
• ISSN: 1573-4056; 1875-6603; 1573-4056
• DOI: 10.2174/0115734056309890240912054616

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. 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. 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. 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.