ABSTRACT
COVID-19 was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.
ABSTRACT
A sudden outbreak of a novel coronavirus SARS-CoV-2 in 2019 has now emerged as a pandemic threatening to efface the existence of mankind. In absence of any valid and appropriate vaccines to combat this newly evolved agent, there is need of novel resource molecules for treatment and prophylaxis. To this effect, flavonol morin which is found in fruits, vegetables and various medicinal herbs has been evaluated for its antiviral potential in the present study. PASS analysis of morin versus reference antiviral drugs baricitinib, remdesivir and hydroxychloroquine revealed that morin displayed no violations of Lipinski's rule of five and other druglikeness filters. Morin also displayed no tumorigenic, reproductive or irritant effects and exhibited good absorption and permeation through GI (clogP <5). In principal component analysis, morin appeared closest to baricitinib in 3D space. Morin displayed potent binding to spike glycoprotein, main protease 3CLPro and papain-like protease PLPro of SARS-CoV-2, SARS-CoV and MERS-CoV using molecular docking and significant binding to three viral-specific host proteins viz. human ACE2, importin-α and poly (ADP-ribose) polymerase (PARP)-1, further lending support to its antiviral efficacy. Additionally, morin displayed potent binding to pro-inflammatory cytokines IL-6, 8 and 10 also supporting its anti-inflammatory activity. MD simulation of morin with SARS-CoV-2 3CLPro and PLPro displayed strong stability at 300 K. Both complexes exhibited constant RMSDs of protein side chains and Cα atoms throughout the simulation run time. In conclusion, morin might hold considerable therapeutic potential for the treatment and management of not only COVID-19, but also SARS and MERS if studied further. Communicated by Ramaswamy H. Sarma.