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Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators...

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Autores principales: Fayemiwo, Michael Adebisi, Olowookere, Toluwase Ayobami, Arekete, Samson Afolabi, Ogunde, Adewale Opeoluwa, Odim, Mba Obasi, Oguntunde, Bosede Oyenike, Olaniyan, Oluwabunmi Omobolanle, Ojewumi, Theresa Omolayo, Oyetade, Idowu Sunday, Aremu, Ademola Adegoke, Kayode, Aderonke Anthonia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356654/
https://www.ncbi.nlm.nih.gov/pubmed/34435093
http://dx.doi.org/10.7717/peerj-cs.614
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author Fayemiwo, Michael Adebisi
Olowookere, Toluwase Ayobami
Arekete, Samson Afolabi
Ogunde, Adewale Opeoluwa
Odim, Mba Obasi
Oguntunde, Bosede Oyenike
Olaniyan, Oluwabunmi Omobolanle
Ojewumi, Theresa Omolayo
Oyetade, Idowu Sunday
Aremu, Ademola Adegoke
Kayode, Aderonke Anthonia
author_facet Fayemiwo, Michael Adebisi
Olowookere, Toluwase Ayobami
Arekete, Samson Afolabi
Ogunde, Adewale Opeoluwa
Odim, Mba Obasi
Oguntunde, Bosede Oyenike
Olaniyan, Oluwabunmi Omobolanle
Ojewumi, Theresa Omolayo
Oyetade, Idowu Sunday
Aremu, Ademola Adegoke
Kayode, Aderonke Anthonia
author_sort Fayemiwo, Michael Adebisi
collection PubMed
description Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.
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spelling pubmed-83566542021-08-24 Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset Fayemiwo, Michael Adebisi Olowookere, Toluwase Ayobami Arekete, Samson Afolabi Ogunde, Adewale Opeoluwa Odim, Mba Obasi Oguntunde, Bosede Oyenike Olaniyan, Oluwabunmi Omobolanle Ojewumi, Theresa Omolayo Oyetade, Idowu Sunday Aremu, Ademola Adegoke Kayode, Aderonke Anthonia PeerJ Comput Sci Bioinformatics Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models’ predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work. PeerJ Inc. 2021-08-03 /pmc/articles/PMC8356654/ /pubmed/34435093 http://dx.doi.org/10.7717/peerj-cs.614 Text en © 2021 Fayemiwo et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Fayemiwo, Michael Adebisi
Olowookere, Toluwase Ayobami
Arekete, Samson Afolabi
Ogunde, Adewale Opeoluwa
Odim, Mba Obasi
Oguntunde, Bosede Oyenike
Olaniyan, Oluwabunmi Omobolanle
Ojewumi, Theresa Omolayo
Oyetade, Idowu Sunday
Aremu, Ademola Adegoke
Kayode, Aderonke Anthonia
Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset
title Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset
title_full Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset
title_fullStr Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset
title_full_unstemmed Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset
title_short Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset
title_sort modeling a deep transfer learning framework for the classification of covid-19 radiology dataset
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356654/
https://www.ncbi.nlm.nih.gov/pubmed/34435093
http://dx.doi.org/10.7717/peerj-cs.614
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