ArticleBiomedical signal processing and control2022
Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.Biomedical signal processing and control · 2023Article
- Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- DCML: Deep contrastive mutual learning for COVID-19 recognition.Biomedical signal processing and control · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
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2 authors.
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Abstract
Automatic classification of cough data can play a vital role in early detection of Covid-19. Lots of Covid-19 symptoms are somehow related to the human respiratory system, which affect sound production organs. As a result, anomalies in cough sound is expected to be discovered in Covid-19 patients as a sign of infection. This drives the research towards detection of potential Covid-19 cases with inspecting cough sound. While there are several well-performing deep networks, which are capable of classifying sound with a high accuracy, they are not suitable for using in early detection of Covid-19 as they are huge and power/memory hungry. Actually, cough recognition algorithms need to be implemented in hand-held or wearable devices in order to generate early Covid-19 warning without the need to refer individuals to health centers. Therefore, accurate and at the same time lightweight classifiers are needed, in practice. So, there is a need to either compress the complicated models or design light-weight models from the beginning which are suitable for implementation on embedded devices. In this paper, we follow the second approach. We investigate a new lightweight deep learning model to distinguish Covid and Non-Covid cough data. This model not only achieves the state of the art on the well-known and publicly available Virufy dataset, but also is shown to be a good candidate for implementation in low-power devices suitable for hand-held applications.
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