A Comprehensive Review of Sound-Based Modalities for Automatic COVID-19 Detection using Deep Learning-Based Techniques
2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-20245166
ABSTRACT
The World Health Organization has labeled the novel coronavirus illness (COVID-19) a pandemic since March 2020. It's a new viral infection with a respiratory tropism that could lead to atypical pneumonia. Thus, according to experts, early detection of the positive cases with people infected by the COVID-19 virus is highly needed. In this manner, patients will be segregated from other individuals, and the infection will not spread. As a result, developing early detection and diagnosis procedures to enable a speedy treatment process and stop the transmission of the virus has become a focus of research. Alternative early-screening approaches have become necessary due to the time-consuming nature of the current testing methodology such as Reverse transcription polymerase chain reaction (RT-PCR) test. The methods for detecting COVID-19 using deep learning (DL) algorithms using sound modality, which have become an active research area in recent years, have been thoroughly reviewed in this work. Although the majority of the newly proposed methods are based on medical images (i.e. X-ray and CT scans), we show in this comprehensive survey that the sound modality can be a good alternative to these methods, providing faster and easiest way to create a database with a high performance. We also present the most popular sound databases proposed for COVID-19 detection. © 2022 IEEE.
COVID-19; deep learning; feature extraction; sound; Computerized tomography; Diagnosis; Medical imaging; Polymerase chain reaction; Viruses; Atypicals; Coronaviruses; Detection and diagnosis; Diagnosis procedure; Features extraction; Focus of researches; Treatment process; Viral infections; World Health Organization
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Diagnostic study
/
Observational study
Language:
English
Journal:
2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022
Year:
2022
Document Type:
Article
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