Acoustery System for Differential Diagnosing of Coronavirus COVID-19 Disease

Mitrofanova, Anastasia; Mikhaylov, Dmitry; Shaznaev, Ilman; Chumanskaia, Vera; Saveliev, V.

doi:10.1109/ojemb.2021.3127078

Cited by 2 publications

(2 citation statements)

References 10 publications

(12 reference statements)

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“…This system was based on deep learning technology and can be used in clinics. A large-scale database that comprises records of coughing was used to test this system [ 19 ]. Tang et al introduced a deep learning model to diagnose COVID-19 using X-ray images.…”

Section: Related Workmentioning

confidence: 99%

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient’s body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people’s behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended.

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Section: Related Workmentioning

confidence: 99%

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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“…In response to the global COVID-19 pandemic, there has been a growing number of publicly available databases of COVID-19 respiratory sounds available [23] , [24] , [25] , [26] , [27] , [28] . This has led to more interest in the evaluation of cough as an indication of disease state [24] , [28] , [29] , [30] , [31] , [32] , [33] , [34] . One area of interest is the transition of the COVID-19 cough from a dry cough, in early stages of the disease, to more wet-like cough in severe cases where the disease directly effects the lungs [35] , [36] , [37] .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

Cohen-McFarlane

Wallace

et al. 2022

IEEE Open J. Eng. Med. Biol.

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Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.

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Acoustery System for Differential Diagnosing of Coronavirus COVID-19 Disease

Cited by 2 publications

References 10 publications

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

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