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A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks

Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so...

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Detalles Bibliográficos
Autores principales: Zhang, Jing, Qiu, Shi, Cui, Xiaohai, Liang, Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249522/
https://www.ncbi.nlm.nih.gov/pubmed/35782073
http://dx.doi.org/10.1155/2022/3341924
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author Zhang, Jing
Qiu, Shi
Cui, Xiaohai
Liang, Ting
author_facet Zhang, Jing
Qiu, Shi
Cui, Xiaohai
Liang, Ting
author_sort Zhang, Jing
collection PubMed
description Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness.
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spelling pubmed-92495222022-07-02 A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks Zhang, Jing Qiu, Shi Cui, Xiaohai Liang, Ting Biomed Res Int Research Article Pulmonary nodules have been found as the main pathological change in the lung. Signs of pulmonary nodule lay the major basis for the recognition of the benign and malignant of pulmonary nodules. The spiculation of pulmonary nodules is one of the main signs. Pulmonary nodules are small in volume, so they are difficult to extract accurately. Moreover, the number of spiculation samples is limited, so it is difficult to build a stable network structure. Thus, a novel pulmonary nodule spiculation recognition algorithm is proposed. MCA (morphological component analysis) model is built to segment pulmonary nodules in accordance with the composition of pulmonary CT images. Subsequently, the maximum density projection mechanism is introduced to characterize the boundary features of pulmonary nodules to the maximum extent. Inspired by time series dynamic programming, this paper proposes DTW (dynamic time warping) distance to measure data similarity. Lastly, a semisupervised generative adversarial network is built to solve the problem of insufficient positive samples, and it is capable of recognizing pulmonary nodule spiculation. As revealed by the experimental result, the proposed algorithm exhibited strong robustness. Hindawi 2022-06-24 /pmc/articles/PMC9249522/ /pubmed/35782073 http://dx.doi.org/10.1155/2022/3341924 Text en Copyright © 2022 Jing Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Jing
Qiu, Shi
Cui, Xiaohai
Liang, Ting
A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks
title A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks
title_full A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks
title_fullStr A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks
title_full_unstemmed A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks
title_short A Pulmonary Nodule Spiculation Recognition Algorithm Based on Generative Adversarial Networks
title_sort pulmonary nodule spiculation recognition algorithm based on generative adversarial networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249522/
https://www.ncbi.nlm.nih.gov/pubmed/35782073
http://dx.doi.org/10.1155/2022/3341924
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