COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models

Loey, Mohamed; Mirjalili, Seyedali

doi:10.1016/j.compbiomed.2021.105020

Cited by 39 publications

(32 citation statements)

References 36 publications

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“…The count of CNN usage comprises the instances where CNNs were used in research regardless of their shape but does not include known architectures cited within articles under the CNN category, such as ResNet-50. The latter is also a popular architecture among the studies surveyed, but it is not the only “borrowed” architecture implemented, In fact, Loey and Mirjalili [ 26 ] juxtaposed ResNet-50 with ResNet-18, ResNet-101, GoogleNet, NASNet, and MobileNet V2. In two articles, CNN and RNN architectures were used and compared.…”

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%

“…Soltanian and Borna [ 28 ] showed that quadratic-based CNNs could provide higher accuracy when compared to “ordinary” CNN. Often, studies used their models to compete against the existing ones in the literature [ 26 , 32 ].…”

Section: Resultsmentioning

confidence: 99%

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

2022

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“…Furthermore, the better performance is acquired with the large number of datasets of MFCCs, it differentiates non- COVID-19 and COVID-19 coughs [ 49 , 50 ]. develops a new facial mask condition identifies methods by combination of pictures by SRCNet (Super resolution and classification networks), calculates three classification problems on unrestrained 2D facial images [ 51 ]. The proposed algorithm includes four steps: facial detection, and cropping, image pre-processing, facemask wearing identification and image super resolution.…”

Section: Biomedical Signal Processingmentioning

confidence: 99%

A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19

Anand

Sharma

Pourush

et al. 2022

Annals of Medicine &Amp; Surgery

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“…The results demonstrate how this technique may be applied to distinguish between pneumonia as well as asthma in public environments. According to [29], the goal of this study is to characterize the unique coughing sounds tones of COVID-19 artefacts in signals from various real-life scenarios. The model provided here tends to take two crucial stages into consideration.…”

Section: Related Workmentioning

confidence: 99%

A New Model to Detect COVID-19 Coughing and Breathing Sound Symptoms Classification from CQT and Mel Spectrogram Image Representation using Deep Learning

Aly¹,

Alotaibi²

2022

IJACSA

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Deep Learning is a relatively new Artificial Intelligence technique that has shown to be extremely effective in a variety of fields. Image categorization and also the identification of artefacts in images are being employed in visual recognition. The goal of this study is to recognize COVID-19 artefacts like cough and also breath noises in signals from realworld situations. The suggested strategy considers two major steps. The first step is a signal-to-image translation that is aided by the Constant-Q Transform (CQT) and a Mel-scale spectrogram method. Next, nine deep transfer models (GoogleNet, ResNet18/34/50/100/101, SqueezeNet, MobileNetv2, and NasNetmobile) are used to extract and also categorise features. The digital audio signal will be represented by the recorded voice. The CQT will transform a time-domain audio input to a frequency-domain signal. To produce a spectrogram, the frequency will really be converted to a log scale as well as the colour dimension will be converted to decibels. To construct a Mel spectrogram, the spectrogram will indeed be translated onto a Mel scale. The dataset contains information from over 1,600 people from all over the world (1185 men as well as 415 women). The suggested DL model takes as input the CQT as well as Melscale spectrograms derived from the breathing and coughing tones of patients diagnosed using the coswara-combined dataset. With the better classification performance employing cough sound CQT and a Mel-spectrogram image, the current proposal outperformed the other nine CNN networks. For patients diagnosed, the accuracy, sensitivity, as well as specificity were 98.9%, 97.3%, and 98.1%, respectively. The Resnet18 is the most reliable network for symptomatic patients using cough and breath sounds. When applied to the Coswara dataset, we discovered that the suggested model's accuracy (98.7%) outperforms the state-of-the-art models (85.6%, 72.9%, 87.1%, and 91.4%) according to the SGDM optimizer. Finally, the research is compared to a comparable investigation. The suggested model is more stable and reliable than any present model. Cough and breathing research precision are good enough just to test extrapolation as well as generalization abilities. As a result, sufferers at their headquarters may utilise this novel method as a main screening tool to try and identify COVID-19 by prioritising patients' RT-PCR testing and decreasing the chance of disease transmission.

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COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models

Cited by 39 publications

References 36 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

A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19

A New Model to Detect COVID-19 Coughing and Breathing Sound Symptoms Classification from CQT and Mel Spectrogram Image Representation using Deep Learning

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