IoU-Based Anchor Box Estimation for Enhanced Lung Region Localization in Chest X-rays Using YOLO v4

OpenAlex · Turkish

Precise lung region detection in chest radiographs is an essential preprocessing step for computer-aided diagnostics. This study presents a YOLO v4–based framework to automatically localize lung regions in posteroanterior (PA) chest X-rays. A subset of the CheXpert dataset, containing 456 manually annotated PA radiographs, was used. Anchor boxes were estimated via an Intersection-over-Union (IoU)–based clustering method, improving scale invariance and shape alignment over Euclidean metrics. Empirical evaluation showed that six anchor boxes achieved the best balance between mean IoU (0.883) and computational efficiency. The trained model was tested on 144 images, yielding Average Precision (AP) of 0.9043 for the lung_region class, which represents only the anatomical lung area and not any specific pathology. The precision–recall curve indicated high precision across most recall values, and the confusion matrix showed 124 true positives, 13 false positives, and 7 false negatives. These results demonstrate that YOLO v4 with optimized anchor box estimation enables accurate, efficient lung region localization, supporting automated radiology workflows.