Efficient Deep Neural Network for an Automated Detection of COVID-19 using CT images
Bibliographic record
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the global population’s health and well-being. Successful screening of infected patients is a crucial step in the battle against COVID-19, with radiology inspection using chest radiography being one of the most popular screening methods. Early studies discovered that patients with COVID-19 infection have anomalies in chest radiography images. In this study, we present our Deep Convolutional Neural Network (CNN) for an automatic detection of COVID-19 using computed tomography (CT). Multiple models are presented and fine-tuned to provide accurate detection of COVID-19 vs. normal vs. pneumonia. The proposed model gives an Area Under Curve (AUC) of 99.64%, an accuracy (ACC) of 96.37%, a specificity of 96.00% and a sensitivity of 97.00%. Moreover, an explainability algorithm has been developed and shows the high efficiency of identifying the pathological signs of COVID-19 in CT scans.
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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) | 0.000 | 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".