A Distribution-based Regression for Real-time COVID-19 Cases Detection from Chest X-ray and CT Images
Bibliographic record
Abstract
The novel coronavirus (COVID-19) that started last December in Wuhan, Hubei Province, China has become a serious healthcare threat with over five million confirmed cases in 215 countries around the world as on May 20. The World Health Organization recommends a rapid diagnosis and immediate isolation of suspected cases. Thus, there is an imminent need to develop an automatic real-time detection system as a quick alternative diagnosis option to control the virus spread. In this work, we propose a regression model based on a flexible distribution called shifted-scaled Dirichlet for real-time detection of coronavirus pneumonia infected patient using chest X-ray radiographs. To derive the parameters of our proposed model, we adopt the maximum likelihood method, where we update the parameters based on the stochastic gradient descent. The experimental results demonstrate that our approach is highly effective for detecting COVID-19 cases and understand the infection on a real-time basis with high accuracy up to 97%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".