Multimodal approach for early prediction of COVID-19 disease using convolutional neural network
Bibliographic record
Abstract
The latest human coronavirus is COVID-19. Chest radiography imaging is essential for screening, early detection, and monitoring COVID-19 infections since the virus resides in the lungs. Classical real time reverse transcriptase polymerase chain reaction (RT-PCR) data and chest X-ray pictures will become more important for COVID-19 identification as the pandemic spreads due to their affordability, wide availability, and infection control benefits, which reduce cross-contamination. This work presents multi-modal hybrid automated approaches to classify COVID-19 illness into three clinical categories: normal, pathogenic, and COVID-19 utilising RT-PCR test data and online chest X-ray datasets. The RT-PCR and chest X-ray image datasets were processed using supervised machine learning and convolutional neural networks (CNN). Together, these measures help us separate COVID-19 patients, those with similar symptoms, and healthy persons. The author improved detection times and classification accuracy with extra tree classifier’s feature selection and openCV’s image sharpening. The proposed approaches were tested using a research dataset. The proposed methods allowed reliable COVID-19 disease categorization for clinical decision-making, with random forest (RF) classifier global precision values of 91.58% on the RT-PCR dataset and CNN model accuracy of 95.46% on improved sharpened images.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".