Covid-19 Classification using a Classical Way of Machine Learning and Deep Learning Techniques

Covid-19, scientifically known as SARC-COV2 a deadly infection caused by virus; it affects many individuals, especially in developing and underdeveloped nations, irrespective with inadequate medical infrastructure every country is suffering with this Wuhan virus. The outbreak of Novel coronavirus disease (COVID-19) was initially noticed in a seafood market in Wuhan city in Hubei Province of China in mid- December, 2019, has now spread to 215 countries/territories/areas worldwide. Early diagnosis of covid-19 is crucial to ensure curative treatment and increase survival rates. Lung CT Scan imaging is the most frequently used method for diagnosing Covid-19. However, the examination of Lung CT Scans is a challenging task and is prone to subjective variability. In this study, we developed a computer-aided diagnosis system for automatic Covid-19 detection using Lung CT Scan images. We employed deep transfer learning to handle the scarcity of available data and designed a Convolutional Neural Network (CNN) model along with the Machine learning methods: Ensemble algorithms (RF), Support Vector Machines (SVM), Recurrent Neural Network(RNN) and Long Short Memory (LSTM). Where, in the existing methods ResNet 50 is used that which did not got the proper accuracy and that tend to be improved. Hence the present method with other transfer learning methods are proposed. The proposed approach was evaluated on publicly available Covid-19 CT scan dataset.

Keywords: Covid-19, Lung CT Scan images. Deep Learning, CNN, RNN, LSTM, SVM, Ensemble Algorithm.