Employing Transfer Learning and LSTM Networks for COVID-19 Detection via Chest X-Ray Imagery
Bibliographic record
Abstract
The recent emergence of COVID-19 has posed substantial challenges to global health sectors.Given the significant impact of the virus on lung tissues, chest radiography has become a crucial tool in the early screening, detection, and continual monitoring of suspected cases.Among several technologies, X-ray imaging stands as a readily available and promising modality for the diagnosis and prognosis of COVID-19.This study presents a methodology for distinguishing between COVID-19 and non-COVID-19 chest X-ray images, leveraging deep feature extraction and pre-trained Convolutional Neural Networks (CNN).Deep features are extracted using the pre-trained deep CNN models and subsequently fed into a Long Short-Term Memory (LSTM) model for end-to-end training.The observational data set comprised 200 X-ray images from non-COVID individuals and 180 from those diagnosed with COVID-19.Image classification was carried out using a variety of models including Visual Geometric Group 16 (VGG16), Visual Geometric Group 19 (VGG19), InceptionV3, Xception, ResNet50, MobileNet, and DenseNet121, yielding average accuracies of 92.7%, 94.46%, 78.1%, 90.6%, 80.7%, 65%, and 93.4% respectively.However, the inclusion of LSTM networks significantly improved the performance of these models in differentiating between COVID-19 and non-COVID-19 cases.This paper conducts a comparative analysis of various CNN models supplemented with an LSTM network, utilizing chest X-ray images.The outcomes of this study suggest that the proposed methodology could potentially aid clinicians in enhancing their diagnostic accuracy concerning COVID-19.
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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 | 0.001 | 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) | 0.000 | 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".