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

By Dilbag Singh, Vijay Kumar, Vaishali, Manjit Kaur. 2020

529 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Infectious Diseases, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020, background): “Most research is conducted on CT images, but many of them do not exploit the 3D information of CT images, such as the work by [10], [13], [14].”
Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (2020, background): “The CT scans of chest display bilateral ground-glass opacity [42].”
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation (2020, unknown): “[35] tried a multi-objective differential evolution–based convolutional neural networks to classify COVID-19 patients from chest CT images.”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “classified the chest computed tomography (CT) images from infected people with and without COVID-19 using multi-objective differential evolution (MODE)-based CNN [17].”
Deep Transfer Learning Based Classification Model for COVID-19 Disease (2020, unknown): “Thus, supervised learning models can be used for COVID-19 patients classification from their respective chest CT images [2].”
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning (2020, background): “The radiological imaging such as chest X-ray and chest CT-scan can be helpful to isolate the infected persons timely and control this epidemic situation (Singh et al., 2020).”
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020, unknown): “Due to low ionizing radiations and portability of X-rays, it has been preferred over the chest CT scan [11].”
A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images (2020, unknown): “To increase nonlinearity in feature maps, the rectified linear unit (ReLU) layer is used [48].”