CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images

Abstract

Background and objective: The Coronavirus 2019, or shortly COVID-19, is a viral disease that causes serious pneumonia and impacts our different body parts from mild to severe depending on patient's immune system. This infection was first reported in Wuhan city of China in December 2019, and afterward, it became a global pandemic spreading rapidly around the world. As the virus spreads through human to human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Preventing viral spreading requires early detection of positive cases and to treat infected patients as quickly as possible. The need for COVID-19 testing kits has increased, and many of the developing countries in the world are facing a shortage of testing kits as new cases are increasing day by day. In this situation, the recent research using radiology imaging (such as X-ray and CT scan) techniques can be proven helpful to detect COVID-19 as X-ray and CT scan images provide important information about the disease caused by COVID-19 virus. The latest data mining and machine learning techniques such as Convolutional Neural Network (CNN) can be applied along with X-ray and CT scan images of the lungs for the accurate and rapid detection of the disease, assisting in mitigating the problem of scarcity of testing kits.

Methods: Hence a novel CNN model called CoroDet for automatic detection of COVID-19 by using raw chest X-ray and CT scan images have been proposed in this study. CoroDet is developed to serve as an accurate diagnostics for 2 class classification (COVID and Normal), 3 class classification (COVID, Normal, and non-COVID pneumonia), and 4 class classification (COVID, Normal, non-COVID viral pneumonia, and non-COVID bacterial pneumonia).

Results: The performance of our proposed model was compared with ten existing techniques for COVID detection in terms of accuracy. A classification accuracy of 99.1% for 2 class classification, 94.2% for 3 class classification, and 91.2% for 4 class classification was produced by our proposed model, which is obviously better than the state-of-the-art-methods used for COVID-19 detection to the best of our knowledge. Moreover, the dataset with x-ray images that we prepared for the evaluation of our method is the largest datasets for COVID detection as far as our knowledge goes.

Conclusion: The experimental results of our proposed method CoroDet indicate the superiority of CoroDet over the existing state-of-the-art-methods. CoroDet may assist clinicians in making appropriate decisions for COVID-19 detection and may also mitigate the problem of scarcity of testing kits.

Keywords: Accuracy; COVID-19; Confusion matrix; Convolutional neural network; Deep learning; Pneumonia-bacterial; Pneumonia-viral; X-ray.

Fig. 1

A flowchart of our proposed CoroDet method.

Fig. 2

COVID-19 sample.

Fig. 3

Normal sample.

Fig. 4

Pneumonia-viral sample.

Fig. 5

Pneumonia-bacterial sample.

Fig. 6

Architecture of proposed 22 layer CNN model.

Fig. 7

Confusion matrix for each fold in 4 class classification.

Fig. 8

Confusion matrix for each fold in 3 class classification.

Fig. 9

Confusion matrix for each fold in 2 class classification.

Fig. 10

Model accuracy graph for 4 class classification.

Fig. 11

Model loss graph for 4 class classification.

Fig. 12

An example on COVID detection using a randomly selected test image.

Fig. 13

An example of Non-COVID detection using a randomly selected test image.

Fig. 14

An example of pneumonia-viral detection using a randomly selected test image.

Fig. 15

An example on pneumonia-bacterial detection using a randomly selected test image.