COVID-19 Detection from Chest X-Ray Images Using Deep Convolutional Neural Networks with Weights Imprinting Approach
Bibliographic record
Abstract
COVID-19 pandemic has drastically changed our lives. Chest radiographyhas been used to detect COVID-19. However, the numberof publicly available COVID-19 x-ray images is extremely limited,resulting in a highly imbalanced dataset. This is a challenge whenusing deep learning for classification and detection. In this work, wepropose the use of pre-trained deep Convolutional Neural Networks(CNN) and integrate them with a few-shot learning approach namedimprinted weights. The integrated model is fine tuned to enhancethe capability of detecting COVID-19. The proposed solution thencombines the fine-tuned models using a weighted average ensemblefor achieving an optimal 82% sensitivity to COVID-19. To thebest of authors’ knowledge, the proposed solution is one of the firstto utilize imprinted weights model with weighted average ensemblefor enhancing the model sensitivity to COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".