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Automatic diagnosis of COPD in lung CT images based on multi-view DCNN

Automatic diagnosis of COPD in lung CT images based on multi-view DCNN
Automatic diagnosis of COPD in lung CT images based on multi-view DCNN

Chronic obstructive pulmonary disease (COPD) has long been one of the leading causes of morbidity and mortality worldwide. Numerous studies have shown that CT image analysis is an effective way to diagnose patients with COPD. Automatic diagnosis of CT images using computer vision will shorten the time a patient takes to confirm COPD. This enables patients to receive timely treatment. CT images are three-dimensional data. The extraction of 3D texture features is the core of classification problem. However, the classification accuracy of the current computer vision models is still not high when extracting these features. Therefore, computer vision assisted diagnosis has not been widely used. In this paper, we proposed MV-DCNN, a multi-view deep neural network based on 15 directions. The experimental results show that compared with the state-of-art methods, this method significantly improves the accuracy of COPD classification, with an accuracy of 97.7%. The model proposed here can be used in the medical institutions for diagnosis of COPD.

COPD, Classification, Deep convolutional neural network, Multi-view
571-578
Scitepress
Bao, Yin
d23b032b-5ea1-4610-b1fe-77f5eb4cd559
Al Makady, Yasseen Hamad
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
De Marsico, Maria
di Baja, Gabriella Sanniti
Fred, Ana
Bao, Yin
d23b032b-5ea1-4610-b1fe-77f5eb4cd559
Al Makady, Yasseen Hamad
8125c167-d6ef-45bf-ac22-b9a7e72d36fd
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
De Marsico, Maria
di Baja, Gabriella Sanniti
Fred, Ana

Bao, Yin, Al Makady, Yasseen Hamad and Mahmoodi, Sasan (2021) Automatic diagnosis of COPD in lung CT images based on multi-view DCNN. De Marsico, Maria, di Baja, Gabriella Sanniti and Fred, Ana (eds.) In ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods. Scitepress. pp. 571-578 .

Record type: Conference or Workshop Item (Paper)

Abstract

Chronic obstructive pulmonary disease (COPD) has long been one of the leading causes of morbidity and mortality worldwide. Numerous studies have shown that CT image analysis is an effective way to diagnose patients with COPD. Automatic diagnosis of CT images using computer vision will shorten the time a patient takes to confirm COPD. This enables patients to receive timely treatment. CT images are three-dimensional data. The extraction of 3D texture features is the core of classification problem. However, the classification accuracy of the current computer vision models is still not high when extracting these features. Therefore, computer vision assisted diagnosis has not been widely used. In this paper, we proposed MV-DCNN, a multi-view deep neural network based on 15 directions. The experimental results show that compared with the state-of-art methods, this method significantly improves the accuracy of COPD classification, with an accuracy of 97.7%. The model proposed here can be used in the medical institutions for diagnosis of COPD.

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PaperDCNN - Accepted Manuscript
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More information

Published date: 4 February 2021
Additional Information: Publisher Copyright: © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved
Keywords: COPD, Classification, Deep convolutional neural network, Multi-view

Identifiers

Local EPrints ID: 445469
URI: http://eprints.soton.ac.uk/id/eprint/445469
PURE UUID: d5977c9b-b294-4d03-a584-caafb3be8f1c
ORCID for Yasseen Hamad Al Makady: ORCID iD orcid.org/0000-0002-1583-1777

Catalogue record

Date deposited: 10 Dec 2020 17:31
Last modified: 01 Nov 2022 05:01

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Contributors

Author: Yin Bao
Author: Yasseen Hamad Al Makady ORCID iD
Author: Sasan Mahmoodi
Editor: Maria De Marsico
Editor: Gabriella Sanniti di Baja
Editor: Ana Fred

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