Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model

Yan Sitao

doi:doi:10.11648/j.sd.20200801.15

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Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model

Yan Sitao

Published in Science Discovery (Volume 8, Issue 1)

Received: 11 April 2020 Accepted: Published: 19 May 2020

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Abstract

Lung cancer is a kind of cancer with high lethality in the world, and the detection of lung nodules becomes particularly important in the early clinical manifestations of lung cancer. However, due to the characteristics of the small size of the lung nodules on the front of the chest and the obstruction of the ribs, it is more difficult to manually detect the lung nodules. At the same time, because of the explosive growth of lung X-ray images and diagnostic reports, the The application of deep learning technology to the identification of lung nodules has become an inevitable. In order to be able to detect lung nodules in real time, this paper is based on the YOLOV3 algorithm. In view of the characteristics of lung nodules imaging, such as small size and rib occlusion, a method that can be used Lung nodule detection algorithm (nodule-YOLOV3), the pre-processed lung nodule X-ray image is input to the nodule-YOLOV3 network to obtain the prediction results of lung nodules. The experimental results show that: nodule -YOLOV3 detection accuracy is 61%, compared with YOLOV3 target detection algorithm, the accuracy of nodule-YOLOV3 algorithm is improved by 3%.

Published in	Science Discovery (Volume 8, Issue 1)
DOI	10.11648/j.sd.20200801.15
Page(s)	18-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Lung Nodule, X-ray Images, Deep Learning, YOLOV3

References

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Yan Sitao. (2020). Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model. Science Discovery, 8(1), 18-23. https://doi.org/10.11648/j.sd.20200801.15

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Yan Sitao. Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model. Sci. Discov. 2020, 8(1), 18-23. doi: 10.11648/j.sd.20200801.15

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Yan Sitao. Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model. Sci Discov. 2020;8(1):18-23. doi: 10.11648/j.sd.20200801.15

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@article{10.11648/j.sd.20200801.15,
  author = {Yan Sitao},
  title = {Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model},
  journal = {Science Discovery},
  volume = {8},
  number = {1},
  pages = {18-23},
  doi = {10.11648/j.sd.20200801.15},
  url = {https://doi.org/10.11648/j.sd.20200801.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20200801.15},
  abstract = {Lung cancer is a kind of cancer with high lethality in the world, and the detection of lung nodules becomes particularly important in the early clinical manifestations of lung cancer. However, due to the characteristics of the small size of the lung nodules on the front of the chest and the obstruction of the ribs, it is more difficult to manually detect the lung nodules. At the same time, because of the explosive growth of lung X-ray images and diagnostic reports, the The application of deep learning technology to the identification of lung nodules has become an inevitable. In order to be able to detect lung nodules in real time, this paper is based on the YOLOV3 algorithm. In view of the characteristics of lung nodules imaging, such as small size and rib occlusion, a method that can be used Lung nodule detection algorithm (nodule-YOLOV3), the pre-processed lung nodule X-ray image is input to the nodule-YOLOV3 network to obtain the prediction results of lung nodules. The experimental results show that: nodule -YOLOV3 detection accuracy is 61%, compared with YOLOV3 target detection algorithm, the accuracy of nodule-YOLOV3 algorithm is improved by 3%.},
 year = {2020}
}

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TY  - JOUR
T1  - Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model
AU  - Yan Sitao
Y1  - 2020/05/19
PY  - 2020
N1  - https://doi.org/10.11648/j.sd.20200801.15
DO  - 10.11648/j.sd.20200801.15
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 18
EP  - 23
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20200801.15
AB  - Lung cancer is a kind of cancer with high lethality in the world, and the detection of lung nodules becomes particularly important in the early clinical manifestations of lung cancer. However, due to the characteristics of the small size of the lung nodules on the front of the chest and the obstruction of the ribs, it is more difficult to manually detect the lung nodules. At the same time, because of the explosive growth of lung X-ray images and diagnostic reports, the The application of deep learning technology to the identification of lung nodules has become an inevitable. In order to be able to detect lung nodules in real time, this paper is based on the YOLOV3 algorithm. In view of the characteristics of lung nodules imaging, such as small size and rib occlusion, a method that can be used Lung nodule detection algorithm (nodule-YOLOV3), the pre-processed lung nodule X-ray image is input to the nodule-YOLOV3 network to obtain the prediction results of lung nodules. The experimental results show that: nodule -YOLOV3 detection accuracy is 61%, compared with YOLOV3 target detection algorithm, the accuracy of nodule-YOLOV3 algorithm is improved by 3%.
VL  - 8
IS  - 1
ER  -

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Author Information

Yan Sitao

School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China

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APA Style

Yan Sitao. (2020). Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model. Science Discovery, 8(1), 18-23. https://doi.org/10.11648/j.sd.20200801.15

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ACS Style

Yan Sitao. Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model. Sci. Discov. 2020, 8(1), 18-23. doi: 10.11648/j.sd.20200801.15

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AMA Style

Yan Sitao. Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model. Sci Discov. 2020;8(1):18-23. doi: 10.11648/j.sd.20200801.15

Copy | Download

@article{10.11648/j.sd.20200801.15,
  author = {Yan Sitao},
  title = {Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model},
  journal = {Science Discovery},
  volume = {8},
  number = {1},
  pages = {18-23},
  doi = {10.11648/j.sd.20200801.15},
  url = {https://doi.org/10.11648/j.sd.20200801.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20200801.15},
  abstract = {Lung cancer is a kind of cancer with high lethality in the world, and the detection of lung nodules becomes particularly important in the early clinical manifestations of lung cancer. However, due to the characteristics of the small size of the lung nodules on the front of the chest and the obstruction of the ribs, it is more difficult to manually detect the lung nodules. At the same time, because of the explosive growth of lung X-ray images and diagnostic reports, the The application of deep learning technology to the identification of lung nodules has become an inevitable. In order to be able to detect lung nodules in real time, this paper is based on the YOLOV3 algorithm. In view of the characteristics of lung nodules imaging, such as small size and rib occlusion, a method that can be used Lung nodule detection algorithm (nodule-YOLOV3), the pre-processed lung nodule X-ray image is input to the nodule-YOLOV3 network to obtain the prediction results of lung nodules. The experimental results show that: nodule -YOLOV3 detection accuracy is 61%, compared with YOLOV3 target detection algorithm, the accuracy of nodule-YOLOV3 algorithm is improved by 3%.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Detection Method of Chest X-ray Lung Nodules Based on Improved YOLOV3 Model
AU  - Yan Sitao
Y1  - 2020/05/19
PY  - 2020
N1  - https://doi.org/10.11648/j.sd.20200801.15
DO  - 10.11648/j.sd.20200801.15
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 18
EP  - 23
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20200801.15
AB  - Lung cancer is a kind of cancer with high lethality in the world, and the detection of lung nodules becomes particularly important in the early clinical manifestations of lung cancer. However, due to the characteristics of the small size of the lung nodules on the front of the chest and the obstruction of the ribs, it is more difficult to manually detect the lung nodules. At the same time, because of the explosive growth of lung X-ray images and diagnostic reports, the The application of deep learning technology to the identification of lung nodules has become an inevitable. In order to be able to detect lung nodules in real time, this paper is based on the YOLOV3 algorithm. In view of the characteristics of lung nodules imaging, such as small size and rib occlusion, a method that can be used Lung nodule detection algorithm (nodule-YOLOV3), the pre-processed lung nodule X-ray image is input to the nodule-YOLOV3 network to obtain the prediction results of lung nodules. The experimental results show that: nodule -YOLOV3 detection accuracy is 61%, compared with YOLOV3 target detection algorithm, the accuracy of nodule-YOLOV3 algorithm is improved by 3%.
VL  - 8
IS  - 1
ER  -

Copy | Download