IoU-Based Anchor Box Estimation for Enhanced Lung Region Localization in Chest X-rays Using YOLO v4
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".