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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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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 |
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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 |
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