ArticleScientific reports2021
An ensemble learning approach to digital corona virus preliminary screening from cough sounds.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.Diagnostics (Basel, Switzerland) · 2024Article
- COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.IEEE journal of biomedical and health informatics · 2024Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- An AI-Enabled Bias-Free Respiratory Disease Diagnosis Model Using Cough Audio.Bioengineering (Basel, Switzerland) · 2024Article
- RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study.Mayo Clinic proceedings. Digital health · 2023Article
- Smartphone-based cough monitoring as a near real-time digital pneumonia biomarker.ERJ open research · 2023Article
- Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection.Scientific data · 2023Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- Transfer learning for the efficient detection of COVID-19 from smartphone audio data.Pervasive and mobile computing · 2023Article
- An evolutionary ensemble learning for diagnosing COVID-19 via cough signals.Intelligent medicine · 2023Article
- Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.Frontiers in artificial intelligence · 2023Article
- Considerations and Challenges for Real-World Deployment of an Acoustic-Based COVID-19 Screening System.Sensors (Basel, Switzerland) · 2022Article
- Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.Computers & electrical engineering : an international journal · 2022Article
- Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method.Computers in biology and medicine · 2022Article
- A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19.Annals of medicine and surgery (2012) · 2022Review
- Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review.IEEE open journal of engineering in medicine and biology · 2022Article
- Graphene-based temperature, humidity, and strain sensor: A review on progress, characterization, and potential applications during Covid-19 pandemic.Sensors international · 2022Article
- A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?PeerJ. Computer science · 2022Article
- Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.PloS one · 2022Article
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5 authors.
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Abstract
This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.
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