ArticleComputers in biology and medicine2022
COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 97 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- The Use of Audio Signals for Detecting COVID-19: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Beyond fingerprint and voice print: cough sequence sound for identity verification.npj health systems · 2026Article
- Conversational speech for respiratory triage in primary care: a pilot study.Frontiers in medicine · 2026Article
- Advanced COVID-19 detection using cough signals with space reconstruction and 3D deep convolutional neural networks.Scientific reports · 2025Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Development and validation of a nomogram for predicting lung cancer based on acoustic-clinical features.Frontiers in medicine · 2025Article
- SympCoughNet: symptom assisted audio-based COVID-19 detection.Frontiers in digital health · 2025Article
- Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning.PloS one · 2025Article
- Cough2COVID-19 detection using an enhanced multi layer ensemble deep learning framework and CoughFeatureRanker.Scientific reports · 2024Article
- Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection.Scientific data · 2023Article
- Co-Design of a Voice-Based Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 (UpcomingVoice Study): Protocol for a Mixed Methods Study.JMIR research protocols · 2023Article
- Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.Biomedical signal processing and control · 2023Article
- Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.Journal of medical Internet research · 2023Article
- COVID-19 and human development: An approach for classification of HDI with deep CNN.Biomedical signal processing and control · 2023Article
- A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms.Mobile networks and applications : MONET · 2023Article
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
- Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.Frontiers in artificial intelligence · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware such as a smartphone. We use datasets that contain cough, sneeze, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50. These pre-trained networks are subsequently either fine-tuned using smaller datasets of coughing with COVID-19 labels in the process of transfer learning, or are used as bottleneck feature extractors. Results show that a Resnet50 classifier trained by this transfer learning process delivers optimal or near-optimal performance across all datasets achieving areas under the receiver operating characteristic (ROC AUC) of 0.98, 0.94 and 0.92 respectively for all three sound classes: coughs, breaths and speech. This indicates that coughs carry the strongest COVID-19 signature, followed by breath and speech. Our results also show that applying transfer learning and extracting bottleneck features using the larger datasets without COVID-19 labels led not only to improved performance, but also to a marked reduction in the standard deviation of the classifier AUCs measured over the outer folds during nested cross-validation, indicating better generalisation. We conclude that deep transfer learning and bottleneck feature extraction can improve COVID-19 cough, breath and speech audio classification, yielding automatic COVID-19 detection with a better and more consistent overall performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.