Automated detection of COVID-19 cases using deep neural networks with X-ray images

Abstract

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.

Keywords: Chest X-ray images; Coronavirus (COVID-19); Deep learning; Radiology images.

Graphical abstract

Fig. 1

Chest X-ray images of a 50-year-old COVID-19 patient with pneumonia over a week [20].

Fig. 2

A few COVID-19 cases and findings by dataset: (a) Cardio-vasal shadow within the limits [54], (b) Increasing left basilar opacity is visible, arousing concern about pneumonia [5], (c) Progressive infiltrate and consolidation [55], (d) Small consolidation in right upper lobe and ground-glass opacities in both lower lobes [56], (e) Infection demonstrates right infrahilar airspace opacities [6], and (f) Progression of prominent bilateral perihilar infiltration and ill-defined patchy opacities at bilateral lungs [57].

Fig. 3

A schematic presentation of convolution and Max-pooling layer operations.

Fig. 4

The architecture of the proposed model (DarkCovidNet).

Fig. 5

Schematic representation of training and validation scheme employed in the 5-fold cross-validation procedure.

Fig. 6

Validation, training loss and validation accuracy curves obtained for DarkCovidNet model in fold-1.

Fig. 7

The overlapped and 5-fold confusion matrix results of the multi-class classification task: (a) overlapped confusion matrix, (b) Fold-1 CM, (c) Fold-2 CM, (d) Fold-3 CM, (e) Fold-4 CM, and (f) Fold-5 CM.

Fig. 8

The overlapped and 5-fold confusion matrix results for the binary classification task: (a) Overlapped confusion matrix, (b) Fold-1 CM, (c) Fold-2 CM, (d) Fold-3 CM, (e) Fold-4 CM, and (f) Fold-5 CM.

Fig. 9

An illustration of performance evaluation of the model outputs by an expert.

Fig. 10

Images evaluated by the radiologist and DarkCovidNet model: (a) Predicted as Pneumonia by model but actual class is COVID-19, (b) Predicted as Pneumonia by model but actual class is No-Findings, (c) Model is correctly detected as multifocal GGO.

Fig. 11

X-ray images and the corresponding heat maps: (a) first X-ray image, (b) heat map of (a), (c) second X-ray image, and (d) heat map of (c).

Fig. 12

Differences observed by the radiologist between some COVID and pneumonia case images.