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COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs
In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186852/ https://www.ncbi.nlm.nih.gov/pubmed/37220492 http://dx.doi.org/10.1016/j.eswa.2023.120477 |
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author | Khan, Saddam Hussain Iqbal, Javed Hassnain, Syed Agha Owais, Muhammad Mostafa, Samih M. Hadjouni, Myriam Mahmoud, Amena |
author_facet | Khan, Saddam Hussain Iqbal, Javed Hassnain, Syed Agha Owais, Muhammad Mostafa, Samih M. Hadjouni, Myriam Mahmoud, Amena |
author_sort | Khan, Saddam Hussain |
collection | PubMed |
description | In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between infection and background. In this regard, a new two-phase deep convolutional neural network (CNN) based diagnostic system is proposed to detect minute irregularities and analyze COVID-19 infection. In the first phase, a novel SB-STM-BRNet CNN is developed, incorporating a new channel Squeezed and Boosted (SB) and dilated convolutional-based Split-Transform-Merge (STM) block to detect COVID-19 infected lung CT images. The new STM blocks performed multi-path region-smoothing and boundary operations, which helped to learn minor contrast variation and global COVID-19 specific patterns. Furthermore, the diverse boosted channels are achieved using the SB and Transfer Learning concepts in STM blocks to learn texture variation between COVID-19-specific and healthy images. In the second phase, COVID-19 infected images are provided to the novel COVID-CB-RESeg segmentation CNN to identify and analyze COVID-19 infectious regions. The proposed COVID-CB-RESeg methodically employed region-homogeneity and heterogeneity operations in each encoder-decoder block and boosted-decoder using auxiliary channels to simultaneously learn the low illumination and boundaries of the COVID-19 infected region. The proposed diagnostic system yields good performance in terms of accuracy: 98.21 %, F-score: 98.24%, Dice Similarity: 96.40 %, and IOU: 98.85 % for the COVID-19 infected region. The proposed diagnostic system would reduce the burden and strengthen the radiologist's decision for a fast and accurate COVID-19 diagnosis. |
format | Online Article Text |
id | pubmed-10186852 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Authors. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101868522023-05-16 COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs Khan, Saddam Hussain Iqbal, Javed Hassnain, Syed Agha Owais, Muhammad Mostafa, Samih M. Hadjouni, Myriam Mahmoud, Amena Expert Syst Appl Article In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between infection and background. In this regard, a new two-phase deep convolutional neural network (CNN) based diagnostic system is proposed to detect minute irregularities and analyze COVID-19 infection. In the first phase, a novel SB-STM-BRNet CNN is developed, incorporating a new channel Squeezed and Boosted (SB) and dilated convolutional-based Split-Transform-Merge (STM) block to detect COVID-19 infected lung CT images. The new STM blocks performed multi-path region-smoothing and boundary operations, which helped to learn minor contrast variation and global COVID-19 specific patterns. Furthermore, the diverse boosted channels are achieved using the SB and Transfer Learning concepts in STM blocks to learn texture variation between COVID-19-specific and healthy images. In the second phase, COVID-19 infected images are provided to the novel COVID-CB-RESeg segmentation CNN to identify and analyze COVID-19 infectious regions. The proposed COVID-CB-RESeg methodically employed region-homogeneity and heterogeneity operations in each encoder-decoder block and boosted-decoder using auxiliary channels to simultaneously learn the low illumination and boundaries of the COVID-19 infected region. The proposed diagnostic system yields good performance in terms of accuracy: 98.21 %, F-score: 98.24%, Dice Similarity: 96.40 %, and IOU: 98.85 % for the COVID-19 infected region. The proposed diagnostic system would reduce the burden and strengthen the radiologist's decision for a fast and accurate COVID-19 diagnosis. The Authors. Published by Elsevier Ltd. 2023-11-01 2023-05-16 /pmc/articles/PMC10186852/ /pubmed/37220492 http://dx.doi.org/10.1016/j.eswa.2023.120477 Text en © 2023 The Authors 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 Khan, Saddam Hussain Iqbal, Javed Hassnain, Syed Agha Owais, Muhammad Mostafa, Samih M. Hadjouni, Myriam Mahmoud, Amena COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs |
title | COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs |
title_full | COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs |
title_fullStr | COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs |
title_full_unstemmed | COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs |
title_short | COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs |
title_sort | covid-19 detection and analysis from lung ct images using novel channel boosted cnns |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186852/ https://www.ncbi.nlm.nih.gov/pubmed/37220492 http://dx.doi.org/10.1016/j.eswa.2023.120477 |
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