Transfer Learning-Based Model for Automated COVID-19 Detection Using Computerized Tomography Scan Graph
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) has had a huge impact on healthcare systems worldwide since 2019. In this study, we discussed how the combination of transfer learning and traditional classifiers performed for image classification of healthy people, COVID-19 patients, pneumonia patients, and lung cancer patients. Methods: Different combinations of image preprocessing methods and transfer learning architectures were tested and evaluated. The best performed combination was chosen as feature extractor. Features was finally classified by support vector machine (SVM) and optimized by particle swarm optimization (PSO) algorithm. Results: When combined VGG16 architecture with the PSO-SVM approach, we obtained exciting results, with 93.5% accuracy in recognition. Conclusions: The experiments’ results suggest VGG16 can reach high accuracy with a small number of epochs. And using VGG16 as a feature extractor then combining it with SVM and appropriate optimization algorithm can improve the classification performance. The new developed classification algorithm may to some extent help clinicians lighten their workload when facing COVID-19 diagnostic problems. Clin Infect Immun. 2022;7(2):37-48 doi: https://doi.org/10.14740/cii154
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".