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A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.

Abstract：

:Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app's deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure.

journal_name

Appl Soft Comput

journal_title

Applied soft computing

authors

Sait U,K V GL,Shivakumar S,Kumar T,Bhaumik R,Prajapati S,Bhalla K,Chakrapani A

doi

10.1016/j.asoc.2021.107522

keywords:

["Breathing sounds","CNN","Chest X-ray images","Covid-19","Deep-learning","MLP"]

subject

Has Abstract

pub_date

2021-09-01 00:00:00

pages

107522

eissn

1568-4946

issn

1872-9681

pii

S1568-4946(21)00445-2

journal_volume

109

pub_type

杂志文章

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