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IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System.

Authors :
Jain, Anurag
Yadav, Kusum
Alharbi, Hadeel Fahad
Tiwari, Shamik
Source :
Computers, Materials & Continua; 2021, Vol. 71 Issue 1, p423-438, 16p
Publication Year :
2022

Abstract

Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage. To minimize the spread of the pandemic, IoT-enabled devices can be used to collect patient's data remotely in a secure manner. Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus. In this work, the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot, IoT, and deep learning technology. In phase one, an artificially assisted chatbot can guide an individual by asking about some common symptoms. In case of detection of even a single sign, the second phase of diagnosis can be considered, consisting of using a thermal scanner and pulse oximeter. In case of high temperature and low oxygen saturation levels, the third phase of diagnosis will be recommended, where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body. The proposed model reduces human intervention through chatbot-based initial screening, sensor-based IoT devices, and deep learning-based X-ray analysis. It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15462218
Volume :
71
Issue :
1
Database :
Complementary Index
Journal :
Computers, Materials & Continua
Publication Type :
Academic Journal
Accession number :
153507468
Full Text :
https://doi.org/10.32604/cmc.2022.020238