CNN Models Using Chest X-Ray Images for COVID-19 Detection: A Survey
Bibliographic record
Abstract
The COVID-19 pandemic, which began in 2019, has spread globally, causing substantial human suffering and economic disruption.A collaborative global effort is essential to combat this disease.Artificial Intelligence has played a pivotal role in this battle, providing numerous deep learning strategies to automate the detection of COVID-19.Among these strategies, Convolutional Neural Network (CNN) models have emerged as a particularly potent tool for COVID-19 detection through the analysis of medical images.The present paper provides a comprehensive survey of various CNN models that have been developed for the classification of X-ray images in the context of COVID-19.These models have been categorized into three groups for the purpose of this review.The first category is centered on models that utilize transfer learning from pre-trained CNN models.The second one consists of Custom CNN Models that have been developed from scratch.The final category, known as Hybrid CNN Models, integrates elements from both of the previous categories.Outlined with details regarding the dataset size, the number of classes considered, the architecture of the model, and the criteria used for performance evaluation, encompassing accuracy, sensitivity, and specificity.This review thus provides a comprehensive overview of the current landscape of CNN models for COVID-19 detection using X-Ray images, offering valuable insights for future research in this critical area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".