Analysis of COVID-19 Infections on a CT Image Using DeepSense Model.

Adil Khadidos,Alaa O Khadidos,Sachi Nandan Mohanty,Yuvaraj Natarajan,Georgios Tsaramirsis,Srihari Kannan

doi:10.3389/fpubh.2020.599550

Abstract

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional 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 the 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 multidimensional 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 DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

Highlights

The novel coronavirus disease 2019 (COVID-19) is a pandemic outbreak [1]
The deep learning model namely DeepSense algorithm is a combination of convolutional neural network (CNN) and recurrent neural network (RNN) designed to improve the performance of the classification accuracy
DeepSense learning is regarded as a module for accurate predictions of lung infections caused by the COVID-19 virus

Summary

INTRODUCTION

The novel coronavirus disease 2019 (COVID-19) is a pandemic outbreak [1]. COVID-19 patients are classified essentially based on computerized tomography (CT) lung images, and it is used widely for testing. The supervised learning models [3,4,5,6,7,8,9,10] can be utilized for classifying the patients from the CT images. A DeepSense algorithm is utilized to diagnose COVID-19 infections among the medical community. The deep learning method is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) that reduces the classifier burden on optimal classification of the multidimensional data features. (a) The authors develop a combined CNN and RNN to classify the medical image datasets. The Results and Discussions section concludes the work with future enhancement

METHODS

RESULTS AND DISCUSSIONS

DATA AVAILABILITY STATEMENT