RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray

Saman Motamed,Patrik Rogalla,Farzad Khalvati

doi:10.1038/s41598-021-87994-2

Abstract

COVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77.

Highlights

COVID-19 spread globally over a short period of time and became a deadly p andemic[1]
The experiments were performed using AnoGAN trained on full COVIDx images, AnoGAN trained on segmented COVIDx images and randomized generative adversarial network (RANDGAN) trained on segmented COVIDx images
We introduced RANDGAN, a novel generative adversarial network for semi-supervised detection of an unknown (COVID-19) class in chest X-ray images from a pool of known (Normal and Pneumonia) and unknown classes (COVID-19) by only using the known classes for training

Summary

Introduction

COVID-19 spread globally over a short period of time and became a deadly p andemic[1]. Supervised architectures require training data with complete labels for all image classes (e.g., normal and COVID-19). This requires accurate labeling of the data for all cases and the cumbersome annotation effort, and the diagnosis variation amongst expert radiologists limits the performance of these supervised models on new data. We propose a semi-supervised generative model (randomized generative adversarial networkRANDGAN) for detection of COVID-19 positive chest X-ray images. To the best of our knowledge, this study is the first of its kind, using semi-supervised learning for detection of COVID-19 in medical images and reporting performance accuracy on the entire cohort of COVID-19 positive images without the need to use any of the COVID-19 positive images to train our model. To train the generative models in this study, all images were converted to gray scale, resized to 128 × 128 pixels and normalized to have pixel intensities in the [− 1, 1] range

Methods

Results

Conclusion