AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath

Dentamaro, Vincenzo; Giglio, Paolo; Impedovo, Donato; Moretti, Luigi; Pirlo, Giuseppe

doi:10.1016/j.patcog.2022.108656

Cited by 31 publications

(16 citation statements)

References 45 publications

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“…In the context of transfer learning, Pahar et al [ 23 ] utilized a total of 11,202 cough sounds from the datasets TASK, Brooklyn, Wallacedene, and Google Audio Set &Freesound. Dentamaro et al [ 24 ] made use of an open-source dataset, UrbanSound 8K, which is comprises 10 classes of street sounds and no cough [ 25 ]. With transfer learning, their model’s AUC improved by approximately 9%.…”

Section: Resultsmentioning

confidence: 99%

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Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

2022

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In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual’s health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets.

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Section: Resultsmentioning

confidence: 99%

“…In addition, the FNR can be straightforwardly computed as

. We note that the confusion matrix, a matrix that counts the TN, FN, TP, and FP from actual target and predicted values, is often available, which can provide the reader with all the necessary tools to make a judgment [ 24 , 40 , 41 , 42 ].…”

Section: Discussionmentioning

confidence: 99%

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

2022

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“…Although there is a significant level of interest in automated COVID-19 detection using cough, sound and speech data, multiple authors report the presence of background noise which, despite having been been reduced using classical audio denoising methods such as spectral noise gating and data augmentation via time stretching, can still persist [121], [140], [141]. However, many different types and combinations of deep learning methods have been employed to resolve the same issue including CNNs [142], RNNs [143]- [145], and GANs [146]- [148].…”

Section: B Waveform Noise and Artifacts (Advanced Approaches)mentioning

confidence: 99%

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

Isgut

Gloster

Choi

et al. 2023

IEEE Rev. Biomed. Eng.

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At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.

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“…The manual approach is laborious, and there are cases where patients forget or deliberately conceal the locations they have visited. The current COVID-19 automatic tracking method can be divided into two categories: tracking based on GPS data [11] , [12] and tracking based on Bluetooth data [13] , [14] , [15] .…”

Section: Related Workmentioning

confidence: 99%

COVID-19 contact tracking by group activity trajectory recovery over camera networks

Wang

et al. 2022

Pattern Recognition

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AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath

Cited by 31 publications

References 45 publications

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

COVID-19 contact tracking by group activity trajectory recovery over camera networks

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