Discriminating COVID-19 from Pneumonia using Machine Learning Algorithms and Chest X-ray Images
Bibliographic record
Abstract
Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is commonly used to detect COVID-19. However, the RT-PCR test necessitates person-to-person contact to administer and is expensive. Not only that, the diagnostic tests are still unreachable to the majority of the global population. The chest X-ray images are helpful for this purpose as the X-ray machines are available in almost all healthcare facilities. However, the chest X-ray images of COVID-19 and viral pneumonia patients are very similar and often lead to misdiagnosis. This paper presents automated noninvasive algorithms that can identify the X-ray images of COVID patients from that of pneumonia patients. This investigation has employed two algorithms based on machine learning and deep learning approaches. The lower dimension encoded features are extracted from the X-ray images and machine learning algorithms are applied. On the other hand, the deep learning algorithm relies on the inbuilt feature extractor networks to classify the original X-ray images. The simulation results show that the proposed algorithms can discriminate COVID patients from pneumonia patients with the best accuracies of 100% and 98.1% based on pre-trained deep learning and machine learning algorithms, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".