Ensembled YOLO for multiorgan detection in chest x-rays

Chest radiographs are a vital tool for identifying pathological changes within the thoracic cavity. Artificial intelligence (AI) and machine learning (ML) driven screening or diagnostic applications require accurate detection of anatomical structures within the Chest X-ray (CXR) image. The You Only Look Once (YOLO) object detection models have recently gained prominence for their efficacy in detecting anatomical structures in medical images. However, state-of-the-art results using it are typically for single anatomical organ detection. Advanced image analysis would benefit from simultaneous detection more than one anatomical organ. In this work we propose a multi-organ detection technique through two recent YOLO versions and their sub-variants. We evaluate their effectiveness in detecting lung and heart regions in CXRs simultaneously. We used the JSRT CXR dataset for internal training, validation, and testing. Further, the generalizability of the models is evaluated using two external test sets, viz., the Montgomery CXR dataset and a subset of the RSNA CXR dataset against available annotations therein. Our evaluation demonstrates that YOLOv9 models notably outperform YOLOv8 variants. We demonstrated further improvements in detection performance through ensemble approaches.