Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 May 18;11(5):895.
doi: 10.3390/diagnostics11050895.

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Yash Karbhari  1 , Arpan Basu  2 , Zong-Woo Geem  3 , Gi-Tae Han  3 , Ram Sarkar  2
Affiliations

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Yash Karbhari et al. Diagnostics (Basel). .

Abstract

COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.

Keywords: COVID-19 detection; chest X-ray; deep learning; feature selection; generative adversarial network; harmony search; synthetic data generation.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Flowchart representing the overall process.
Figure 2
Figure 2
Schematic diagram of ACGAN.
Figure 3
Figure 3
A schematic diagram of the skip connections in the ResNet architecture. (He et al. [22]).
Figure 4
Figure 4
A schematic diagram representing an inception block. (Chollet [23]).
Figure 5
Figure 5
A schematic diagram representing a simplified inception block. Compared to Figure 4, it only contains a single size of convolutions (3×3) and does not contain pooling layers. (Chollet [23]).
Figure 6
Figure 6
A strictly equivalent reformulation of the simplified inception block of Figure 5. Chollet [23].
Figure 7
Figure 7
A flowchart representing the HS algorithm.
Figure 8
Figure 8
The loss curve for a single CNN model (VGG16) for 20 epochs.
Figure 9
Figure 9
Some synthetic images that were generated using the present approach. (a) Synthetic images belonging to the COVID-19 infected CXR class generated by the GAN; (b) synthetic images belonging to the normal CXR class generated by the GAN.
See this image and copyright information in PMC

References

    1. Nicholls S.M., Poplawski R., Bull M.J., Underwood A., Chapman M., Abu-Dahab K., Taylor B., Jackson B., Rey S., Amato R., et al. MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance. bioRxiv. 2020 doi: 10.1101/2020.10.06.328328. - DOI - PMC - PubMed
    1. Chen N., Zhou M., Dong X., Qu J., Gong F., Han Y., Qiu Y., Wang J., Liu Y., Wei Y., et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed
    1. Wong A., Lin Z., Wang L., Chung A., Shen B., Abbasi A., Hoshmand-Kochi M., Duong T. Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays. Sci. Rep. 2021;11:1–8. doi: 10.1038/s41598-021-88538-4. - DOI - PMC - PubMed
    1. Toussie D., Voutsinas N., Finkelstein M., Cedillo M.A., Manna S., Maron S.Z., Jacobi A., Chung M., Bernheim A., Eber C., et al. Clinical and chest radiography features determine patient outcomes in young and middle-aged adults with COVID-19. Radiology. 2020;297:E197–E206. doi: 10.1148/radiol.2020201754. - DOI - PMC - PubMed
    1. Shen B., Hoshmand-Kochi M., Abbasi A., Glass S., Jiang Z., Singer A., Thode H., Li H., Hou W., Duong T. Initial chest radiograph scores inform COVID-19 status, intensive care unit admission and need for mechanical ventilation. Clin. Radiol. 2021;76:473.e1–473.e7. doi: 10.1016/j.crad.2021.02.005. - DOI - PMC - PubMed

LinkOut - more resources