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. 2022;3(2):150.
doi: 10.1007/s42979-022-01035-x. Epub 2022 Feb 3.

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19

Debanjan Das  1 , Sagnik Ghosal  2 , Saraju P Mohanty  3
Affiliations

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19

Debanjan Das et al. SN Comput Sci. 2022.

Abstract

The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work, we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called "CoviLearn") to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, and X-rays images can be used to analyze the health of a patient's lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 98.98% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.

Keywords: COVID-19; Deep neural network (DNN); Healthcare-Cyber-Physical System (H-CPS); Machine learning; Smart healthcare; X-ray.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interests.

Figures

Fig. 1
Fig. 1
Process flow of proposed COVID-19 classification
Fig. 2
Fig. 2
Schematic representation of the Healthcare Cyber-Physical System (H-CPS) ecosystem concept for combating COVID-19
Fig. 3
Fig. 3
The proposed next-generation X-ray device of CoviLearn integrated with machine learning models
Fig. 4
Fig. 4
Organization of the DNN with classification layers
Fig. 5
Fig. 5
Confusion matrix for a DNN I, b DNN II, c DNN III, and d DNN IV
Fig. 6
Fig. 6
Comparison of the receiver-operating characteristics (ROC)
Fig. 7
Fig. 7
Classification accuracy in the deep learning system validation
Fig. 8
Fig. 8
Binary cross entropy loss in the deep learning system validation
See this image and copyright information in PMC

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