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Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images
The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418990/ https://www.ncbi.nlm.nih.gov/pubmed/34508976 http://dx.doi.org/10.1016/j.compbiomed.2021.104835 |
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author | Pathan, Sameena Siddalingaswamy, P.C. Kumar, Preetham Pai M M, Manohara Ali, Tanweer Acharya, U. Rajendra |
author_facet | Pathan, Sameena Siddalingaswamy, P.C. Kumar, Preetham Pai M M, Manohara Ali, Tanweer Acharya, U. Rajendra |
author_sort | Pathan, Sameena |
collection | PubMed |
description | The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application. |
format | Online Article Text |
id | pubmed-8418990 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84189902021-09-07 Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images Pathan, Sameena Siddalingaswamy, P.C. Kumar, Preetham Pai M M, Manohara Ali, Tanweer Acharya, U. Rajendra Comput Biol Med Article The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application. Elsevier Ltd. 2021-10 2021-09-06 /pmc/articles/PMC8418990/ /pubmed/34508976 http://dx.doi.org/10.1016/j.compbiomed.2021.104835 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Pathan, Sameena Siddalingaswamy, P.C. Kumar, Preetham Pai M M, Manohara Ali, Tanweer Acharya, U. Rajendra Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images |
title | Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images |
title_full | Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images |
title_fullStr | Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images |
title_full_unstemmed | Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images |
title_short | Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images |
title_sort | novel ensemble of optimized cnn and dynamic selection techniques for accurate covid-19 screening using chest ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418990/ https://www.ncbi.nlm.nih.gov/pubmed/34508976 http://dx.doi.org/10.1016/j.compbiomed.2021.104835 |
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