Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
Bibliographic record
Abstract
Novel coronavirus disease 2019 (Corona Virus Disease 2019, COVID-19) is rampant all over the world, threatening human life and health. Currently, the detection of the presence of nucleic acid from SARS-CoV-2 is mainly based on the nucleic acid test as the standard. However, this method not only takes up a lot of medical resources but also takes a long time to achieve detection results. According to medical analysis, the surface protein of the novel coronavirus can invade the respiratory epithelial cells of patients and cause severe inflammation of the respiratory system, making the cough of COVID-19 patients different from that of healthy people. In this study, the cough sound is used as a large-scale pre-screening method before the nucleic acid test. Firstly, the Mel spectrum features, Mel Frequency Cepstral Coefficients, and VGG embeddings features of cough sound are extracted and oversampling technology is used to balance the dataset for classes with a small number of samples. In terms of the model, we designed multi-headed convolutional neural networks to predict audio samples, and adopted an early stop method to avoid the over-fitting problem of the model. The performance of the model is measured by the binary cross-entropy loss function. Our model performs well on the dataset of the AICovidVN 115M challenge that its accuracy rate is 98.1%, and on the dataset of the University of Cambridge that its accuracy rate is 91.36%.
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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) | 0.002 | 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 | 0.001 | 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".