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Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection
The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641994/ https://www.ncbi.nlm.nih.gov/pubmed/34868521 http://dx.doi.org/10.1155/2021/5359084 |
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author | Huang, Tinglong Zheng, Xuelan He, Lisui Chen, Zhiliang |
author_facet | Huang, Tinglong Zheng, Xuelan He, Lisui Chen, Zhiliang |
author_sort | Huang, Tinglong |
collection | PubMed |
description | The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with convolutional neural network (CNN) algorithm. Then, it was applied to CT image diagnosis of 100 patients with severe lung infection in The Second Affiliated Hospital of Fujian Medical University hospital and compared with traditional methods, and its sensitivity, specificity, and accuracy were compared. It was found that the single training time and loss of U-Net + DC algorithm were reduced by 59.4% and 9.8%, respectively, compared with CNN algorithm, while Dice increased by 3.6%. The lung contour segmented by the proposed model was smooth, which was the closest to the gold standard. Fungal infection, bacterial infection, viral infection, tuberculosis infection, and mixed infection accounted for 28%, 18%, 7%, 7%, and 40%, respectively. 36%, 38%, 26%, 17%, and 20% of the patients had ground-glass shadow, solid shadow, nodule or mass shadow, reticular or linear shadow, and hollow shadow in CT, respectively. The incidence of various CT characteristics in patients with fungal and bacterial infections was statistically significant (P < 0.05). The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + DC algorithm were significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant (P < 0.05). The network of the algorithm in this study demonstrated excellent image segmentation effect. The CT image based on the U-Net + DC algorithm can be used for the diagnosis of patients with severe pulmonary infection, with high diagnostic value. |
format | Online Article Text |
id | pubmed-8641994 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-86419942021-12-04 Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection Huang, Tinglong Zheng, Xuelan He, Lisui Chen, Zhiliang J Healthc Eng Research Article The study aimed to explore the diagnostic value of computed tomography (CT) images based on cavity convolution U-Net algorithm for patients with severe pulmonary infection. A new lung CT image segmentation algorithm (U-Net+ deep convolution (DC)) was proposed based on U-Net network and compared with convolutional neural network (CNN) algorithm. Then, it was applied to CT image diagnosis of 100 patients with severe lung infection in The Second Affiliated Hospital of Fujian Medical University hospital and compared with traditional methods, and its sensitivity, specificity, and accuracy were compared. It was found that the single training time and loss of U-Net + DC algorithm were reduced by 59.4% and 9.8%, respectively, compared with CNN algorithm, while Dice increased by 3.6%. The lung contour segmented by the proposed model was smooth, which was the closest to the gold standard. Fungal infection, bacterial infection, viral infection, tuberculosis infection, and mixed infection accounted for 28%, 18%, 7%, 7%, and 40%, respectively. 36%, 38%, 26%, 17%, and 20% of the patients had ground-glass shadow, solid shadow, nodule or mass shadow, reticular or linear shadow, and hollow shadow in CT, respectively. The incidence of various CT characteristics in patients with fungal and bacterial infections was statistically significant (P < 0.05). The specificity (94.32%) and accuracy (97.22%) of CT image diagnosis based on U-Net + DC algorithm were significantly higher than traditional diagnostic method (75.74% and 74.23%), and the differences were statistically significant (P < 0.05). The network of the algorithm in this study demonstrated excellent image segmentation effect. The CT image based on the U-Net + DC algorithm can be used for the diagnosis of patients with severe pulmonary infection, with high diagnostic value. Hindawi 2021-11-26 /pmc/articles/PMC8641994/ /pubmed/34868521 http://dx.doi.org/10.1155/2021/5359084 Text en Copyright © 2021 Tinglong Huang 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 Huang, Tinglong Zheng, Xuelan He, Lisui Chen, Zhiliang Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection |
title | Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection |
title_full | Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection |
title_fullStr | Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection |
title_full_unstemmed | Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection |
title_short | Diagnostic Value of Deep Learning-Based CT Feature for Severe Pulmonary Infection |
title_sort | diagnostic value of deep learning-based ct feature for severe pulmonary infection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641994/ https://www.ncbi.nlm.nih.gov/pubmed/34868521 http://dx.doi.org/10.1155/2021/5359084 |
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