Detection of COVID-19 on Chest X-Ray Images using Inverted Residuals Structure-Based Convolutional Neural Networks
Bibliographic record
Abstract
China officially reported the COVID-19 coronavirus's existence to the World Health Organization (WHO) on December 31, 2019. Since then, it has spread and has infected millions of people around the world. COVID-19 is a highly contagious disease and it can cause severe respiratory distress. In severe cases it can result in failure of the function of organs simultaneously. Recent studies have shown that chest X-rays of patients suffering from COVID-19 show the specific characteristics of those infected with the virus. This paper presents a method to detect the presence of COVID-19 on chest X-ray images based on inverted residuals structure implemented in MobileNetV2 as a base model. We also explore the performance of using a Fully connected layer with dropout and using the Global Average Pooling layer as top layers of the base model to classify each image into COVID-19 or NonCOVID-19. Our proposed method was able to achieve COVID-19 detection with the best accuracy of 0.81, with precision, recall, and F1-score of 0.81, 0.75, and 0.77, respectively, using the Global Average Pooling layer with data augmentation version.
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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 | 0.002 |
| 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) | 0.000 | 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".