Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

Abstract

The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.

Keywords: COVID-19; Chest X-ray (CXR); Community-acquired pneumonia (CAP); Computer-aided detection (CAD); Deep learning (DL).

Fig. 1

Flow diagram of the proposed CAD system illustrating the discrimination and localization of COVID-19 from CAP on chest X-ray radiographs. We utilized the Discrimination-DL to distinguish COVID-19 from CAP on chest X-rays, and the Localization-DL was trained to detect lung localization (i.e., left lung or right lung or bipulmonary). Abbreviations: Healthy: healthy controls; CAP: community-acquired pneumonia; Left: left lung; Right: right lung.

Fig. 2

Deep learning architectures. (1). We utilized the proposal of a lung regressor (PoL) with superpixel to generate the Discrimination-DL input. The PoL matrix is a 2 × 4 vector, which illustrates the bipulmonary region coordinates. (2). The Discrimination-DL adopts a feature pyramid network as a backbone network on top of a ResNet architecture and generates a differentiated probability across cohort categories. (3). The Localization-DL constructed attention modules use a state-of-the-art residual attention network basic unit. The located region is defined as a 1 × 2 vector and represents all potential pulmonary locations. Abbreviations: PoL: proposal of lung regressor; CAP: community-acquired pneumonia; Left/L: left lung; Right/R: right lung; Bilateral: bipulmonary.

Algorithm 1

Training procedure for Discrimination-DL.

Algorithm 2

Training procedure for Localization-DL.

Algorithm 3

Testing procedure for the computer-aided diagnosis scheme.

Fig. 3

Performance of discriminating COVID-19 from CAP on testing subset. (A). ROC curve for the Discrimination-DL with radiologists for performance comparison. The area under the ROC of the Discrimination-DL was 99%. (B). ROC curves for COVID-19 from CAP on the testing subset trained with Discrimination-DL were 1 for COVID-19, 1 for healthy controls, and 0.99 for CAP. Abbreviations: receiver operating characteristic (ROC) curve (AUC); accuracy (Acc); community-acquired pneumonia (CAP); healthy controls (Healthy).

Fig. 4

Representative chest X-ray radiographs corresponding to Grad-CAM images.

Fig. 5

Performance of localizing infected pulmonary on the testing subset. (A). The ROC curve for Localization-DL compared with radiologist performance. The area under the curve was 93%. (B). The ROC curve of each case with the trained Localization-DL was 0.92 for left pulmonary, 0.93 for right pulmonary and 0.87 for bipulmonary. Abbreviations: receiver operating characteristic (ROC) curve (AUC); accuracy (Acc); left pulmonary (left); right pulmonary (right); bipulmonary (Bilaterel).

Fig. 6

Several examples illustrating that the high-level part features with attention masks.