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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...

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Autores principales: Karim, Faizan, Shah, Munam Ali, Khattak, Hasan Ali, Ameer, Zoobia, Shoaib, Umar, Rauf, Hafiz Tayyab, Al-Turjman, Fadi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2022
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.
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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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