Int J Med Sci 2020; 17(10):1439-1448. doi:10.7150/ijms.46684

Research Paper

Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia

Saleh Albahli

Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

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Citation:
Albahli S. Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia. Int J Med Sci 2020; 17(10):1439-1448. doi:10.7150/ijms.46684. Available from http://www.medsci.org/v17p1439.htm

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Abstract

Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as “Public health emergency of international concerns”. The current situation of Covid-19 and chest related diseases have already gone through radical change with the advancements of image processing tools. There is no effective method which can accurately identify all chest related diseases and tackle the multiple class problems with reliable results.

Method: There are many potentially impactful applications of Deep Learning to fighting the Covid-19 from Chest X-Ray/CT Images, however, most are still in their early stages due to lack of data sharing as it continues to inhibit overall progress in a variety of medical research problems. Based on COVID-19 radiographical changes in CT images, this work aims to detect the possibility of COVID-19 in the patient. This work provides a significant contribution in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results.

Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides effective analysis of chest related diseases with respect to age and gender. Our model achieves 89% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results.

Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.

Keywords: Deep learning, Coronavirus, X-ray, Chest diseases, ResNet-152, Inception-V3