Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images

Abstract

Coronavirus (which is also known as COVID-19) is severely impacting the wellness and lives of many across the globe. There are several methods currently to detect and monitor the progress of the disease such as radiological image from patients’ chests, measuring the symptoms and applying polymerase chain reaction (RT-PCR) test. X-ray imaging is one of the popular techniques used to visualise the impact of the virus on the lungs. Although manual detection of this disease using radiology images is more popular, it can be time-consuming, and is prone to human errors. Hence, automated detection of lung pathologies due to COVID-19 utilising deep learning (Bowles et al.) techniques can assist with yielding accurate results for huge databases. Large volumes of data are needed to achieve generalizable DL models; however, there are very few public databases available for detecting COVID-19 disease pathologies automatically. Standard data augmentation method can be used to enhance the models’ generalizability. In this research, the Extensive COVID-19 X-ray and CT Chest Images Dataset has been used and generative adversarial network (GAN) coupled with trained, semi-supervised CycleGAN (SSA- CycleGAN) has been applied to augment the training dataset. Then a newly designed and finetuned Inception V3 transfer learning model has been developed to train the algorithm for detecting COVID-19 pandemic. The obtained results from the proposed Inception-CycleGAN model indicated Accuracy = 94.2%, Area under Curve = 92.2%, Mean Squared Error = 0.27, Mean Absolute Error = 0.16. The developed Inception-CycleGAN framework is ready to be tested with further COVID-19 X-Ray images of the chest.