Deep learning based detection and analysis of COVID-19 on chest X-ray images

Abstract

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

Keywords: CNN; Chest X-ray images; Covid-19; Deep-learning; Inception net 3; ResNeXt; XCeption.

Fig. 1

Proposed model for chest X-ray dataset evaluation

Fig. 2

Abstract form of Inception Net V3 Model

Fig. 3

Abstracted form of Xception model

Fig. 4

ResNeXt model architecture

Fig. 5

(a) Training and Testing Loss of Xception Net with successive epochs. (b) Training and Testing Accuracy of Xception Net with successive epochs

Fig. 6

(a): Confusion matrix of train data of Xception model. (b): Confusion matrix of test data of the Xception model

Fig. 7

(a): Training and Testing Loss of the Inception V3. (b) Training and Testing Accuracy of an Inception V3

Fig. 8.

(a) Confusion matrix of train data of Inception V3. (b): Confusion matrix of test data of Inception V3

Fig. 9

(a) Training and Testing Loss of ResNeXt model. (b) Training and Testing Accuracy of ResNeXt model

Fig. 10

(a). Confusion matrix of train data of ResNeXt model. (b): Confusion matrix of test data of ResNeXt model