Classification of COVID-19 Cases from X-Ray Images Based on a Modified VGG-16 Model
Bibliographic record
Abstract
COVID-19 is considered one of the most deadly pandemics by the World Health Organization and has claimed the lives of millions around the world. Mechanisms for early diagnosis and detection of this rapidly spreading disease are necessary to save lives. However, the increase in COVID-19 cases requires not relying on traditional means of detecting diseases due to these tests’ limitations and high costs. One diagnostic technique for COVID-19 is X-rays and CT scans. For accurate and highly efficient diagnosis, computer-aided diagnosis is required. In this research, we suggest a convolutional neural network for chest x-ray images categorisation into two classes of infection: COVID-19 and normal. The suggested model uses an upgraded model based on the VGG-16 architecture that has been trained end-to-end on a dataset composed of X-ray images obtained from two different public data repositories, which include 1,320 and 1,578 cases in the COVID-19 and normal classes, respectively. This suggested model was trained and evaluated on the provided dataset and showed that our proposed model showed improved performance in the matter of overall accuracy, recall, precision, and F1-score at 99.54%, 99.5%, 99.5%, and 99.5%, respectively. The system’s significance is supported because it has greater accuracy than other contemporary deep learning methods in the literature on COVID-19 identification.
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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) | 0.003 | 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".