Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning

Amjad Rehman; Tariq Sadad; Tanzila Saba; Ayyaz Hussain; Usman Tariq

doi:10.1109/MITP.2020.3042379

Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning

Amjad Rehman
, Tariq Sadad
, Tanzila Saba
, Ayyaz Hussain
, Usman Tariq

Management Information Systems

Research output: Contribution to journal › Article › peer-review

42 Scopus citations

Abstract

The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computer-aided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN) -based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.

Original language	English
Article number	9520229
Pages (from-to)	57-62
Number of pages	6
Journal	IT Professional
Volume	23
Issue number	4
DOIs	https://doi.org/10.1109/MITP.2020.3042379
State	Published - 1 Jul 2021

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title = "Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning",

abstract = "The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computer-aided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN) -based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98\% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.",

author = "Amjad Rehman and Tariq Sadad and Tanzila Saba and Ayyaz Hussain and Usman Tariq",

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AU - Rehman, Amjad

AU - Sadad, Tariq

AU - Saba, Tanzila

AU - Hussain, Ayyaz

AU - Tariq, Usman

PY - 2021/7/1

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Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning

Abstract

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