Chest Diseases Classification Using CXR and Deep Ensemble Learning
Bibliographic record
Abstract
Chest diseases are among the most common worldwide health problems; they are potentially life-threatening disorders which can affect organs such as lungs and heart. Radiologists typically use visual inspection to diagnose chest X-ray (CXR) diseases, which is a difficult task prone to errors. The signs of chest abnormalities appear as opacities around the affected organ, making it difficult to distinguish between diseases of superimposed organs. To this end, we propose a very first method for CXR organ disease detection using deep learning. We used an ensemble learning (EL) approach to increase the efficiency of the classification of CXR diseases by organs (lung and heart) using a consolidated dataset. This dataset contains 26,316 CXR images from VinDr-CXR and CheXpert datasets. The proposed ensemble of deep convolutional neural networks (DCNN) approach achieves excellent performance with an AUC of 0.9489 for multi-class classification, outperforming many state-of-the-art models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".