Analysis of COVID-19 Infections on a CT Image Using DeepSense Model - Khadidos, Adil; Khadidos, Alaa O.; Kannan, Srihari; Natarajan, Yuvaraj; Mohanty, Sachi Nandan; Tsaramirsis, Georgios (Frontiers in Public Health, 2020)
In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with covid-19 virus. The hybrid deep learning model is designed as a combination of convolution neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of covid-19 infections from lungs. The computerized tomography image is used as an input data and hence the classifier is designed to ease the process of classification on learning the multi-dimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSenseclassifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. It specifically provides the quality of the diagnostic method adopted for the prediction of covid-19 infections in a patient.