COVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation

Mahanta, Saranga Kingkor; Kaushik, Darsh; Jain, Shubham; Truong, Hoang Van; Guha, Koushik

doi:10.1109/icacfct53978.2021.9837350

Cited by 11 publications

(2 citation statements)

References 16 publications

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“…The utilization of the respiratory data has shown remarkable results in the COVID-19 detection. In [9], authors have proposed a deep learning-based system named CovNet, that can perform analysis and classifications on the cough sound recordings of both positive and negative COVID-19 acoustic samples. The Mel Frequency Cepstral Coefficients (MFCCs) have been utilized as the feature input for the proposed model.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Electrocardiograms Classification using CNN Models

Shahin

Nassif

Alsabek

2021

2021 14th International Conference on Developments in eSystems Engineering (DeSE)

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With the periodic rise and fall of COVID-19 and numerous countries being affected by its ramifications, there has been a tremendous amount of work that has been done by scientists, researchers, and doctors all over the world. Prompt intervention is keenly needed to tackle the unconscionable dissemination of the disease. The implementation of Artificial Intelligence (AI) has made a significant contribution to the digital health district by applying the fundamentals of deep learning algorithms. In this study, a novel approach is proposed to automatically diagnose the COVID-19 by the utilization of Electrocardiogram (ECG) data with the integration of deep learning algorithms, specifically the Convolutional Neural Network (CNN) models. Several CNN models have been utilized in this proposed framework, including VGG16, VGG19, InceptionResnetv2, InceptionV3, Resnet50, and Densenet201. The VGG16 model has outperformed the rest of the models, with an accuracy of 85.92%. Our results show a relatively low accuracy in the rest of the models compared to the VGG16 model, which is due to the small size of the utilized dataset, in addition to the exclusive utilization of the Grid search hyperparameters optimization approach for the VGG16 model only. Moreover, our results are preparatory, and there is a possibility to enhance the accuracy of all models by further expanding the dataset and adapting a suitable hyperparameters optimization technique.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Electrocardiograms Classification using CNN Models

Shahin

Nassif

Alsabek

2021

2021 14th International Conference on Developments in eSystems Engineering (DeSE)

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“…In order to identify COVID-19, Mohammed et al [5] presented an AI-based cough sound identification approach after discovering a high degree of consistency in the Mel-frequency cepstral coefficients (MFCC) of cough sounds from COVID-19infected patients. In order to extract and recognize characteristics from cough noises, Siddharth et al [6] employed the ResNet50 network and obtained an AUC of 0.76 on the test set. In a comparison of COVID-19 detection methods using cough sounds and respiratory sounds, Jing Han et al [7] [8] created a classifier for the DiCOVA2021 challenge that used lung X-ray pictures and audio information from coughing.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Intelligent Detection System Based on Cloud-Edge Fusion Recognition Model

Xie,

Li,

Zhou

et al. 2023

J. Phys.: Conf. Ser.

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Analyzing cough sounds can help with the quick detection of COVID-19. A cloud-edge deep learning fusion-based intelligent detection system for COVID-19 is proposed in this paper. In the cloud-side, a COVID-19 detection model based on ResNet18 is employed, with log-Mel-spectrum characteristics used as inputs. In the edge-side, a COVID-19 detection model based on TCNN is developed using raw audio inputs. To improve the detection accuracy, result fusion is carried out in the cloud-side after getting the recognition results from both models. On the test dataset, the fusion model attained a sensitivity of 0.8012, an AUC of 0.8251, and a specificity of 0.7255. According to comparative testing results, the fusion model outperforms the other models in classification performance and is less prone to false-positive errors. It provides a novel way to COVID-19 recognition and performs well as an auxiliary detection method.

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