Analysis of convolutional recurrent neural network classifier for COVID-19 symptoms over computerised tomography images

Abstract

In this paper, a Convolutional Recurrent Neural Network (CRNN) model is designed to classify the patients with COVID-19 infections. The CRNN model is designed to identify the Computerised Tomography (CT) images. The processing of CRNN is modelled with input image processing and feature extraction using CNN and prediction by RNN model that quickens the entire process. The simulation is carried with a set of 226 CT images by varying the training-testing accuracy on a tenfold cross-validation. The accuracy in estimating the image samples is increased with increased training data. The results of the simulation show that the proposed method has higher accuracy and reduced MSE with higher training data than other methods.