Classification of CXR Chest Diseases by Ensembling Deep Learning Models
Bibliographic record
Abstract
Chest related diseases are the most frequent health issues worldwide. They are mainly diagnosed by radiologists using a visual inspection of a chest X-ray (CXR) radiography. This task is challenging and error-prone because of the similarity between signs of the diseases, which occur as opacities around the infected organs. An early diagnosis of chest abnormalities is an essential step in the treatment process. Computer-aided detection (CAD) systems can be useful as decision support tools for radiologists. In recent years, chest disease detection using deep learning (DL) and CXR images has become one of the most promising topics in healthcare. In this work, we propose an approach using an ensemble of deep convolutional neural networks (DCNN) for a two-steps classification of CXR images. In the first step, a CXR image is taken as input of our model. This latter determines whether it is a heart or a lung abnormality. Then, it performs a binary classification for diseases of the same organ as a second step. A dataset of 26,316 CXR images was consolidated by merging images from two open-access datasets (VinDr-CXR and CheXpert). Our approach showed impressive results, obtaining an AUC of 0.9489 for infected organ classification (multi-class classification) and a mean AUC of 0.9957 for specific diseases' classification (binary classification) surpassing state-of-the-art DCNN models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".