Diagnosis of Covid-19 Using Hybrid Ensembled Convolutional Neural Networks

Abstract

COVID-19 has arisen as a worldwide pandemic and has the potential to cause social, economic, and political problems that are catastrophic. Corona virus disease 2019 (COVID-19) has been a problem for the world, including India, notably during its second wave ever since it first appeared in December 2019. Disease caused by the Corona Virus in 2019, which was triggered by an initial case of severe acute respiratory syndrome caused by the Corona Virus 2. (SARS-CoV-2). An effective screening for this infection may facilitate the rapid and effective detection of COVID-19, which in turn can relieve some of the strain on the medical care system. An in-depth analysis of the provided dataset may enable the construction of one-of-a-kind and distinct types of AI computations, which, after being shown, can be subjected to additional processing and evaluation. A hybrid ensemble classifier was suggested in this research by coordinating Random Forest with SVM (Support Vector Machines), and CNN. In the accompanying case study, the suggested model was successful against a broad variety of machine learning methods, including SVM, Decision Tree, KNN, and Logistic Regression.