Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

Publication date: Apr 27, 2020

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals’ precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Concepts Keywords
CNN Neural networks
Computed Tomography Convolutional neural networks
Convolutional Neural Networks Computational neuroscience
Coronavirus Artificial neural networks
Epidemic Artificial intelligence
Infection Computational statistics
PCR Computer vision
Radiology Convolutional neural network
RT Machine learning
Cybernetics
Academic disciplines
Tomography

Semantics

Type Source Name
disease MESH infection

Original Article

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