A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping b...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Scientific reports Ročník 11; číslo 1; s. 34 - 12
Hlavní autoři: Ho, Thao Thi, Kim, Taewoo, Kim, Woo Jin, Lee, Chang Hyun, Chae, Kum Ju, Bak, So Hyeon, Kwon, Sung Ok, Jin, Gong Yong, Park, Eun-Kee, Choi, Sanghun
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 08.01.2021
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1564
631/114/2397
631/1647/245
639/166/985
Allergic diseases
Chronic obstructive pulmonary disease
Classification
Computed tomography
Deep learning
Emphysema
Humanities and Social Sciences
Lung diseases
Mapping
multidisciplinary
Neural networks
Obstructive lung disease
Respiratory tract diseases
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-79336-5