Cascading AB-YOLOv5 and PB-YOLOv5 for rib fracture detection in frontal and oblique chest X-ray images

Abstract

Convolutional deep learning models have shown comparable performance to radiologists in detecting and classifying thoracic diseases. However, research on rib fractures remains limited compared to other thoracic abnormalities. Moreover, existing deep learning models primarily focus on using frontal chest X-ray (CXR) images. To address these gaps, the authors utilised the EDARib-CXR dataset, comprising 369 frontal and 829 oblique CXRs. These X-rays were annotated by experienced radiologists, specifically identifying the presence of rib fractures using bounding-box-level annotations. The authors introduce two detection models, AB-YOLOv5 and PB-YOLOv5, and train and evaluate them on the EDARib-CXR dataset. AB-YOLOv5 is a modified YOLOv5 network that incorporates an auxiliary branch to enhance the resolution of feature maps in the final convolutional network layer. On the other hand, PB-YOLOv5 maintains the same structure as the original YOLOv5 but employs image patches during training to preserve features of small objects in downsampled images. Furthermore, the authors propose a novel two-level cascaded architecture that integrates both AB-YOLOv5 and PB-YOLOv5 detection models. This structure demonstrates improved metrics on the test set, achieving an AP30 score of 0.785. Consequently, the study successfully develops deep learning-based detectors capable of identifying and localising fractured ribs in both frontal and oblique CXR images.