Cargando…

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

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 recurre...

Descripción completa

Detalles Bibliográficos
Autores principales: Khadidos, Adil, Khadidos, Alaa O., Kannan, Srihari, Natarajan, Yuvaraj, Mohanty, Sachi Nandan, Tsaramirsis, Georgios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714903/
https://www.ncbi.nlm.nih.gov/pubmed/33330341
http://dx.doi.org/10.3389/fpubh.2020.599550
_version_ 1783618829629259776
author Khadidos, Adil
Khadidos, Alaa O.
Kannan, Srihari
Natarajan, Yuvaraj
Mohanty, Sachi Nandan
Tsaramirsis, Georgios
author_facet Khadidos, Adil
Khadidos, Alaa O.
Kannan, Srihari
Natarajan, Yuvaraj
Mohanty, Sachi Nandan
Tsaramirsis, Georgios
author_sort Khadidos, Adil
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7714903
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-77149032020-12-15 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 Front Public Health Public Health 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. Frontiers Media S.A. 2020-11-20 /pmc/articles/PMC7714903/ /pubmed/33330341 http://dx.doi.org/10.3389/fpubh.2020.599550 Text en Copyright © 2020 Khadidos, Khadidos, Kannan, Natarajan, Mohanty and Tsaramirsis. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Khadidos, Adil
Khadidos, Alaa O.
Kannan, Srihari
Natarajan, Yuvaraj
Mohanty, Sachi Nandan
Tsaramirsis, Georgios
Analysis of COVID-19 Infections on a CT Image Using DeepSense Model
title Analysis of COVID-19 Infections on a CT Image Using DeepSense Model
title_full Analysis of COVID-19 Infections on a CT Image Using DeepSense Model
title_fullStr Analysis of COVID-19 Infections on a CT Image Using DeepSense Model
title_full_unstemmed Analysis of COVID-19 Infections on a CT Image Using DeepSense Model
title_short Analysis of COVID-19 Infections on a CT Image Using DeepSense Model
title_sort analysis of covid-19 infections on a ct image using deepsense model
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714903/
https://www.ncbi.nlm.nih.gov/pubmed/33330341
http://dx.doi.org/10.3389/fpubh.2020.599550
work_keys_str_mv AT khadidosadil analysisofcovid19infectionsonactimageusingdeepsensemodel
AT khadidosalaao analysisofcovid19infectionsonactimageusingdeepsensemodel
AT kannansrihari analysisofcovid19infectionsonactimageusingdeepsensemodel
AT natarajanyuvaraj analysisofcovid19infectionsonactimageusingdeepsensemodel
AT mohantysachinandan analysisofcovid19infectionsonactimageusingdeepsensemodel
AT tsaramirsisgeorgios analysisofcovid19infectionsonactimageusingdeepsensemodel