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

• Published in: Computerized medical imaging and graphics Vol. 90; p. 101921
• Main Author: Younis, Mohammed Chachan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2021; Elsevier Science Ltd
• Subjects: Artificial neural networks; Betacoronavirus - classification; Convolutional Neural Networks; COVID-19 - epidemiology; COVID-19 - mortality; COVID-19 - virology; Deep Learning; Evaluation; Humans; Internal Medicine; LSTM; Machine learning; Middle East Respiratory Syndrome Coronavirus - classification; NCOV; Neural networks; Other; Pandemics; Patients; Pneumonia, Viral - epidemiology; Pneumonia, Viral - mortality; Pneumonia, Viral - virology; SARS-CoV-2 - classification; Time series; VGG-16; Viral diseases; Viruses; Italy; NCOV; LSTM; Convolutional Neural Networks; VGG-16
• ISSN: 0895-6111; 1879-0771; 1879-0771
• DOI: 10.1016/j.compmedimag.2021.101921

•Novel corona-virus (nCOV), has been declared as a pandemic that started from the Wuhan city of China.•The whole world needs an automated system to classify nCOV.•In this research, Convolutional Neural Network-based models have been employed for the Classification of corona-virus species.•Three classes have been taken as SARS_MERS, nCOV and Normal.•We also employed LSTM model for time series forecasting of nCOV cases in the next 10 days in Italy. 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.