Cargando…
Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection
This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436326/ https://www.ncbi.nlm.nih.gov/pubmed/37602316 http://dx.doi.org/10.3389/fncom.2023.1115167 |
_version_ | 1785092297628581888 |
---|---|
author | Ming, Mao Lu, Na Qian, Wei |
author_facet | Ming, Mao Lu, Na Qian, Wei |
author_sort | Ming, Mao |
collection | PubMed |
description | This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted to the hospital were selected as research subjects and underwent CT imaging scans. The empty convolution (EC) and U-net phase were combined to construct an EC-U-net, which was applied to process the CT images. The results showed that the learning rate of the EC-U-net model decreased substantially with increasing training times until it stabilized and reached zero after 40 training times. The segmentation result of the EC-U-net model for the CT image was very similar to that of the mask image, except for some deviations in edge segmentation. The EC-U-net model exhibited a significantly smaller cross-entropy loss function (CELF) and a higher Dice coefficient than the CNN algorithm. The diagnostic accuracy of CT images based on the EC-U-net model for severe pneumonia complicated with pulmonary infection was substantially higher than that of CT images alone, while the false negative rate (FNR) and false positive rate (FPR) were substantially lower (P < 0.05). Moreover, the true positive rates (TPRs) of CT images based on the EC-U-net model for patchy high-density shadows, diffuse ground glass density shadows, pleural effusion, and lung consolidation were obviously higher than those of the original CT images (P < 0.05). In short, the EC-U-net model was superior to the traditional algorithm regarding the overall performance of CT image segmentation, which can be clinically applied. CT images based on the EC-U-net model can clearly display pulmonary infection lesions, improve the clinical diagnosis of severe pneumonia complicated with pulmonary infection, and help to screen early pulmonary infection and carry out symptomatic treatment. |
format | Online Article Text |
id | pubmed-10436326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104363262023-08-19 Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection Ming, Mao Lu, Na Qian, Wei Front Comput Neurosci Neuroscience This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted to the hospital were selected as research subjects and underwent CT imaging scans. The empty convolution (EC) and U-net phase were combined to construct an EC-U-net, which was applied to process the CT images. The results showed that the learning rate of the EC-U-net model decreased substantially with increasing training times until it stabilized and reached zero after 40 training times. The segmentation result of the EC-U-net model for the CT image was very similar to that of the mask image, except for some deviations in edge segmentation. The EC-U-net model exhibited a significantly smaller cross-entropy loss function (CELF) and a higher Dice coefficient than the CNN algorithm. The diagnostic accuracy of CT images based on the EC-U-net model for severe pneumonia complicated with pulmonary infection was substantially higher than that of CT images alone, while the false negative rate (FNR) and false positive rate (FPR) were substantially lower (P < 0.05). Moreover, the true positive rates (TPRs) of CT images based on the EC-U-net model for patchy high-density shadows, diffuse ground glass density shadows, pleural effusion, and lung consolidation were obviously higher than those of the original CT images (P < 0.05). In short, the EC-U-net model was superior to the traditional algorithm regarding the overall performance of CT image segmentation, which can be clinically applied. CT images based on the EC-U-net model can clearly display pulmonary infection lesions, improve the clinical diagnosis of severe pneumonia complicated with pulmonary infection, and help to screen early pulmonary infection and carry out symptomatic treatment. Frontiers Media S.A. 2023-08-04 /pmc/articles/PMC10436326/ /pubmed/37602316 http://dx.doi.org/10.3389/fncom.2023.1115167 Text en Copyright © 2023 Ming, Lu and Qian. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ming, Mao Lu, Na Qian, Wei Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection |
title | Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection |
title_full | Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection |
title_fullStr | Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection |
title_full_unstemmed | Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection |
title_short | Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection |
title_sort | evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436326/ https://www.ncbi.nlm.nih.gov/pubmed/37602316 http://dx.doi.org/10.3389/fncom.2023.1115167 |
work_keys_str_mv | AT mingmao evaluationofcomputedtomographyimagesunderdeeplearninginthediagnosisofseverepulmonaryinfection AT luna evaluationofcomputedtomographyimagesunderdeeplearninginthediagnosisofseverepulmonaryinfection AT qianwei evaluationofcomputedtomographyimagesunderdeeplearninginthediagnosisofseverepulmonaryinfection |