Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.
Journal:
European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology
Published Date:
Apr 27, 2020
Abstract
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.
Authors
Keywords
Betacoronavirus
Clinical Laboratory Techniques
Coronavirus Infections
COVID-19
COVID-19 Testing
Deep Learning
False Negative Reactions
False Positive Reactions
Humans
Image Interpretation, Computer-Assisted
Lung
Neural Networks, Computer
Pandemics
Pneumonia, Viral
Reverse Transcriptase Polymerase Chain Reaction
SARS-CoV-2
Sensitivity and Specificity
Tomography, X-Ray Computed