Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT\n Images: A Machine Learning-Based Approach
Bibliographic record
Abstract
The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is\nhighly transmittable and pathogenic with no clinically approved antiviral drug\nor vaccine available for treatment. The most common symptoms of COVID-19 are\ndry cough, sore throat, and fever. Symptoms can progress to a severe form of\npneumonia with critical complications, including septic shock, pulmonary edema,\nacute respiratory distress syndrome and multi-organ failure. While medical\nimaging is not currently recommended in Canada for primary diagnosis of\nCOVID-19, computer-aided diagnosis systems could assist in the early detection\nof COVID-19 abnormalities and help to monitor the progression of the disease,\npotentially reduce mortality rates. In this study, we compare popular deep\nlearning-based feature extraction frameworks for automatic COVID-19\nclassification. To obtain the most accurate feature, which is an essential\ncomponent of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3,\nInceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep\nconvolutional neural networks. The extracted features were then fed into\nseveral machine learning classifiers to classify subjects as either a case of\nCOVID-19 or a control. This approach avoided task-specific data pre-processing\nmethods to support a better generalization ability for unseen data. The\nperformance of the proposed method was validated on a publicly available\nCOVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature\nextractor with Bagging tree classifier achieved the best performance with 99%\nclassification accuracy. The second-best learner was a hybrid of the a ResNet50\nfeature extractor trained by LightGBM with an accuracy of 98%.\n
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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".