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Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases
Machine Learning and computer vision have been the frontiers of the war against the COVID-19 Pandemic. Radiology has vastly improved the diagnosis of diseases, especially lung diseases, through the early assessment of key disease factors. Chest X-rays have thus become among the commonly used radiolo...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9153181/ https://www.ncbi.nlm.nih.gov/pubmed/35662915 http://dx.doi.org/10.1016/j.asoc.2022.109077 |
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author | Karim, Faizan Shah, Munam Ali Khattak, Hasan Ali Ameer, Zoobia Shoaib, Umar Rauf, Hafiz Tayyab Al-Turjman, Fadi |
author_facet | Karim, Faizan Shah, Munam Ali Khattak, Hasan Ali Ameer, Zoobia Shoaib, Umar Rauf, Hafiz Tayyab Al-Turjman, Fadi |
author_sort | Karim, Faizan |
collection | PubMed |
description | Machine Learning and computer vision have been the frontiers of the war against the COVID-19 Pandemic. Radiology has vastly improved the diagnosis of diseases, especially lung diseases, through the early assessment of key disease factors. Chest X-rays have thus become among the commonly used radiological tests to detect and diagnose many lung diseases. However, the discovery of lung disease through X-rays is a significantly challenging task depending on the availability of skilled radiologists. There has been a recent increase in attention to the design of Convolution Neural Networks (CNN) models for lung disease classification. A considerable amount of training dataset is required for CNN to work, but the problem is that it cannot handle translation and rotation correctly as input. The recently proposed Capsule Networks (referred to as CapsNets) are new automated learning architecture that aims to overcome the shortcomings in CNN. CapsNets are vital for rotation and complex translation. They require much less training information, which applies to the processing of data sets from medical images, including radiological images of the chest X-rays. In this research, the adoption and integration of CapsNets into the problem of chest X-ray classification have been explored. The aim is to design a deep model using CapsNet that increases the accuracy of the classification problem involved. We have used convolution blocks that take input images and generate convolution layers used as input to capsule block. There are 12 capsule layers operated, and the output of each capsule is used as an input to the next convolution block. The process is repeated for all blocks. The experimental results show that the proposed architecture yields better results when compared with the existing CNN techniques by achieving a better area under the curve (AUC) average. Furthermore, DNet checks the best performance in the ChestXray-14 data set on traditional CNN, and it is validated that DNet performs better with a higher level of total depth. |
format | Online Article Text |
id | pubmed-9153181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91531812022-05-31 Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases Karim, Faizan Shah, Munam Ali Khattak, Hasan Ali Ameer, Zoobia Shoaib, Umar Rauf, Hafiz Tayyab Al-Turjman, Fadi Appl Soft Comput Article Machine Learning and computer vision have been the frontiers of the war against the COVID-19 Pandemic. Radiology has vastly improved the diagnosis of diseases, especially lung diseases, through the early assessment of key disease factors. Chest X-rays have thus become among the commonly used radiological tests to detect and diagnose many lung diseases. However, the discovery of lung disease through X-rays is a significantly challenging task depending on the availability of skilled radiologists. There has been a recent increase in attention to the design of Convolution Neural Networks (CNN) models for lung disease classification. A considerable amount of training dataset is required for CNN to work, but the problem is that it cannot handle translation and rotation correctly as input. The recently proposed Capsule Networks (referred to as CapsNets) are new automated learning architecture that aims to overcome the shortcomings in CNN. CapsNets are vital for rotation and complex translation. They require much less training information, which applies to the processing of data sets from medical images, including radiological images of the chest X-rays. In this research, the adoption and integration of CapsNets into the problem of chest X-ray classification have been explored. The aim is to design a deep model using CapsNet that increases the accuracy of the classification problem involved. We have used convolution blocks that take input images and generate convolution layers used as input to capsule block. There are 12 capsule layers operated, and the output of each capsule is used as an input to the next convolution block. The process is repeated for all blocks. The experimental results show that the proposed architecture yields better results when compared with the existing CNN techniques by achieving a better area under the curve (AUC) average. Furthermore, DNet checks the best performance in the ChestXray-14 data set on traditional CNN, and it is validated that DNet performs better with a higher level of total depth. Elsevier B.V. 2022-07 2022-05-31 /pmc/articles/PMC9153181/ /pubmed/35662915 http://dx.doi.org/10.1016/j.asoc.2022.109077 Text en © 2022 Elsevier B.V. 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 Karim, Faizan Shah, Munam Ali Khattak, Hasan Ali Ameer, Zoobia Shoaib, Umar Rauf, Hafiz Tayyab Al-Turjman, Fadi Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases |
title | Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases |
title_full | Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases |
title_fullStr | Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases |
title_full_unstemmed | Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases |
title_short | Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases |
title_sort | towards an effective model for lung disease classification: using dense capsule nets for early classification of lung diseases |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9153181/ https://www.ncbi.nlm.nih.gov/pubmed/35662915 http://dx.doi.org/10.1016/j.asoc.2022.109077 |
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