Highly Accurate COVID Detection and Classification from CT and X-Ray Images Using Layer Recurrent Neural Network

Abstract

COVID-19 is a significant pandemic that severely attacked many countries. Studies state that earlier stage detection of COVID can reduce the risk of mortality to a great extent. Imposing artificial intelligence technology like Computer vision algorithms can be applied to observe chest X-rays and Computed Tomography (CT) images in order to predict the presence of coronavirus. CT and X-Ray images can provide clear information about the affected lungs and their fluid contents. In the present work, a Convolutional neural network-based technique is proposed to determine the COVID-19 prediction process. Initially, pre-processing is performed to increase the information level and reduce unwanted pixel noise by performing a suitable filtering process. In that stage, the contrast enhancement process equalizes the pixel values to increase the information level. A standard image database is used to perform training and testing processes separately. The system's performance is evaluated based on performance metrics such as Accuracy, Sensitivity, and specificity.