Evaluation of Deep Transfer Learning Methodologies on the COVID-19 Radiographic Chest Images
Bibliographic record
Abstract
In 2019, the world had been attacked with a severe situation by the new version of the SARSCOV- 2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1- score, and Matthew\"s correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID- 19.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".