An Evaluation of Pre-Trained Convolutional Neural Network Models for the Detection of COVID-19 and Pneumonia from Chest X-Ray Imagery
Bibliographic record
Abstract
COVID-19, a global pandemic, has precipitated millions of fatalities worldwide.Concurrently, pneumonia, another perilous disease, continues to affect a vast global population.Diagnosis of COVID-19 can potentially be expedited through image processing techniques applied to chest X-ray (CXR) images.Innovative methodologies such as deep learning and computer vision offer a revolutionary approach to image recognition with minimal human input.This study aims to employ deep learning, specifically convolutional neural networks (CNN), for the detection of COVID-19 and pneumonia.The dataset under scrutiny comprises 9,208 CXR images, distributed across three distinct classes: 3,207 normal (35%), 1,281 COVID-19 (14%), and 4,657 pneumonia (51%).This dataset was subdivided into training and validation data, with an 80% allocation for training and 20% for validation.The approach adopted involved pre-training modifications before validation through data testing.Eight pre-trained models were comparatively analyzed: MobileNet V3 Small, VGG 19, EfficientNet V2 B0, VGG 16, EfficientNet V2 B3, ResNet RS152, EfficientNet V2 Small, and Inception V3.The MobileNet V3 Small model exhibited superior performance, achieving an accuracy of 0.9815.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".