Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction

Abstract

Novel corona-virus (nCOV) has been declared as a pandemic that started from the city Wuhan of China. This deadly virus is infecting people rapidly and has targeted 4.93 million people across the world, with 227 K people being infected only in Italy. Cases of nCOV are quickly increasing whereas the number of nCOV test kits available in hospitals are limited. Under these conditions, an automated system for the classification of patients into nCOV positive and negative cases, is a much needed tool against the pandemic, helping in a selective use of the limited number of test kits. In this research, Convolutional Neural Network-based models (one block VGG, two block VGG, three block VGG, four block VGG, LetNet-5, AlexNet, and Resnet-50) have been employed for the detection of Corona-virus and SARS_MERS infected patients, distinguishing them from the healthy subjects, using lung X-ray scans, which has proven to be a challenging task, due to overlapping characteristics of different corona virus types. Furthermore, LSTM model has been used for time series forecasting of nCOV cases, in the following 10 days, in Italy. The evaluation results obtained, proved that the VGG1 model distinguishes the three classes at an accuracy of almost 91%, as compared to other models, whereas the approach based on the LSTM predicts the number of nCOV cases with 99% accuracy.

Keywords: Convolutional Neural Networks; LSTM; NCOV; VGG-16.

Fig. 1

Male vs. Female COVID-19 deaths (W. Health Organization, 2020).

Fig. 2

Comparison study of COVID-19, SARS and MERS mortality rate (nbcnews, 2020).

Fig. 3

Architecture of LeNet-5 containing maximum 256 vector length.

Fig. 4

VGG-16 network architecture with 5 convolutional layers.

Fig. 5

Inception model network architecture that can be combined with the VGG-16.

Fig. 6

Resnet architecture with 5 convolutional layers.

Fig. 7

Samples of X-ray images dataset regarding 3 underlying classes.

Fig. 8

Accuracy curves for VGG1 model.

Fig. 9

Obtained confusion matrix for VGG1 model.

Fig. 10

AlexNet model – loss curve for 100 epochs.

Fig. 11

Obtained confusion matrix for AlexNet model.

Fig. 12

Accuracy curves for ResNet-50 model.

Fig. 13

Obtained confusion matrix for ResNet-50 model.

Fig. 14

Summary of nCOV confirmed, recovered and death cases from January 22, 2020 to May 20, 2020, in Italy.

Fig. 15

Line curves for predicted vs. actual cases obtained by employing LSTM.