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Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices
Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pand...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7440966/ https://www.ncbi.nlm.nih.gov/pubmed/34764547 http://dx.doi.org/10.1007/s10489-020-01826-w |
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author | Ahuja, Sakshi Panigrahi, Bijaya Ketan Dey, Nilanjan Rajinikanth, Venkatesan Gandhi, Tapan Kumar |
author_facet | Ahuja, Sakshi Panigrahi, Bijaya Ketan Dey, Nilanjan Rajinikanth, Venkatesan Gandhi, Tapan Kumar |
author_sort | Ahuja, Sakshi |
collection | PubMed |
description | Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives. |
format | Online Article Text |
id | pubmed-7440966 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-74409662020-08-21 Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices Ahuja, Sakshi Panigrahi, Bijaya Ketan Dey, Nilanjan Rajinikanth, Venkatesan Gandhi, Tapan Kumar Appl Intell (Dordr) Article Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives. Springer US 2020-08-21 2021 /pmc/articles/PMC7440966/ /pubmed/34764547 http://dx.doi.org/10.1007/s10489-020-01826-w Text en © Springer Science+Business Media, LLC, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Ahuja, Sakshi Panigrahi, Bijaya Ketan Dey, Nilanjan Rajinikanth, Venkatesan Gandhi, Tapan Kumar Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices |
title | Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices |
title_full | Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices |
title_fullStr | Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices |
title_full_unstemmed | Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices |
title_short | Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices |
title_sort | deep transfer learning-based automated detection of covid-19 from lung ct scan slices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7440966/ https://www.ncbi.nlm.nih.gov/pubmed/34764547 http://dx.doi.org/10.1007/s10489-020-01826-w |
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