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. 2022 Feb 1:2022:7672196.
doi: 10.1155/2022/7672196. eCollection 2022.

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

S V Kogilavani  1 , J Prabhu  2 , R Sandhiya  1 , M Sandeep Kumar  2 , UmaShankar Subramaniam  3   4 , Alagar Karthick  5 , M Muhibbullah  6 , Sharmila Banu Sheik Imam  7
Affiliations

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

S V Kogilavani et al. Comput Math Methods Med. .

Retraction in

Abstract

SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body's respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient's computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with "COVID" and "Non-COVID." The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

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Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
Xception architecture.
Figure 2
Figure 2
VGG16 architecture.
Figure 3
Figure 3
MobileNet architecture.
Figure 4
Figure 4
NASNet architecture.
Figure 5
Figure 5
DenseNet121 architecture.
Figure 6
Figure 6
Efficient net architecture.
Figure 7
Figure 7
Proposed system architecture.
Figure 8
Figure 8
Confusion matrix of CNN models.
Figure 9
Figure 9
Epochs versus loss graph for all CNN models.
Figure 10
Figure 10
Epochs versus accuracy graph for all CNN models.
Figure 11
Figure 11
Accuracies obtained for all CNN models.
See this image and copyright information in PMC

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