Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images

Kaiquan Chen; Zhide Li

doi:doi:10.11648/j.ijbse.20221002.11

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Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images

Kaiquan Chen, Zhide Li

Published in International Journal of Biomedical Science and Engineering (Volume 10, Issue 2)

Received: 5 February 2022 Accepted: 26 February 2022 Published: 9 April 2022

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Abstract

Breast cancer is the most common cancer in women. At present, the methods of examining lesions are generally mammography, or B-scanning and other methods with certain radioactive sources, which may lead to aggravation of breast lesions in young women. The dynamic optical breast scanning method uses infrared light to avoid the harm of X-rays to the human body. According to current research, doctors can use Dynamic Optical Breast Image (DOBI) to determine whether a patient has breast cancer. Studies have shown that convolutional neural networks (CNN) have higher detection accuracy in determining whether a patient has breast cancer. In this paper, we use an artificial intelligence neural network approach to analyze and process dynamic optical breast lesion images: we model the clinical lesion breast images in 3D, and use the VoxelMorph algorithm to segment the 3D images into 2D images; The time, space, location, and pathological trend curves in the image are analyzed and processed. We compared classification, sensitivity, and specific characteristics with the original dynamic breast lesion image scoring analysis system. The experimental results show that the accuracy is improved. At the same time, the problem of the original system's signature in the ROI area and leaf curve is solved. The use of CNN improves the analysis and processing speed, shortens the processing time, and increases the accuracy of the diagnostic reference from 83% to 90%.

Published in	International Journal of Biomedical Science and Engineering (Volume 10, Issue 2)
DOI	10.11648/j.ijbse.20221002.11
Page(s)	38-43
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

Dynamic Optical Breast Lesion Image, Artificial Intelligence Neural Network, CNN Method

References

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[3]	Melekoodappattu, J. G., Subbian, P. S. “Automated breast cancer detection using hybrid extreme learning machine classifier.” J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02359-3.
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[8]	Wenming Yang, Zirui Wang, Kaiquan Chen, Zhide Li and Qingmin Liao, “A Comprehensive Classification System for Breast Cancer Diagnosis Based on Dynamic Optical Breast Imaging” 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4741-4744. 2019.
[9]	Milletari, F., Navab, N., Ahmadi, S. A.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: Proc. 3DV. pp. 565–571 (2016)
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[15]	Cheng, L., et al. "Comparison of dynamic optical breast imaging (DOBI) and mammography in sensitivity, specificity and safety of breast cancer diagnosis: a prospective analysis of 62 patients in two centers. " Journal of Peking University (2011).

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APA Style

Kaiquan Chen, Zhide Li. (2022). Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images. International Journal of Biomedical Science and Engineering, 10(2), 38-43. https://doi.org/10.11648/j.ijbse.20221002.11

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ACS Style

Kaiquan Chen; Zhide Li. Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images. Int. J. Biomed. Sci. Eng. 2022, 10(2), 38-43. doi: 10.11648/j.ijbse.20221002.11

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AMA Style

Kaiquan Chen, Zhide Li. Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images. Int J Biomed Sci Eng. 2022;10(2):38-43. doi: 10.11648/j.ijbse.20221002.11

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@article{10.11648/j.ijbse.20221002.11,
  author = {Kaiquan Chen and Zhide Li},
  title = {Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {10},
  number = {2},
  pages = {38-43},
  doi = {10.11648/j.ijbse.20221002.11},
  url = {https://doi.org/10.11648/j.ijbse.20221002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20221002.11},
  abstract = {Breast cancer is the most common cancer in women. At present, the methods of examining lesions are generally mammography, or B-scanning and other methods with certain radioactive sources, which may lead to aggravation of breast lesions in young women. The dynamic optical breast scanning method uses infrared light to avoid the harm of X-rays to the human body. According to current research, doctors can use Dynamic Optical Breast Image (DOBI) to determine whether a patient has breast cancer. Studies have shown that convolutional neural networks (CNN) have higher detection accuracy in determining whether a patient has breast cancer. In this paper, we use an artificial intelligence neural network approach to analyze and process dynamic optical breast lesion images: we model the clinical lesion breast images in 3D, and use the VoxelMorph algorithm to segment the 3D images into 2D images; The time, space, location, and pathological trend curves in the image are analyzed and processed. We compared classification, sensitivity, and specific characteristics with the original dynamic breast lesion image scoring analysis system. The experimental results show that the accuracy is improved. At the same time, the problem of the original system's signature in the ROI area and leaf curve is solved. The use of CNN improves the analysis and processing speed, shortens the processing time, and increases the accuracy of the diagnostic reference from 83% to 90%.},
 year = {2022}
}

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TY  - JOUR
T1  - Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images
AU  - Kaiquan Chen
AU  - Zhide Li
Y1  - 2022/04/09
PY  - 2022
N1  - https://doi.org/10.11648/j.ijbse.20221002.11
DO  - 10.11648/j.ijbse.20221002.11
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 38
EP  - 43
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20221002.11
AB  - Breast cancer is the most common cancer in women. At present, the methods of examining lesions are generally mammography, or B-scanning and other methods with certain radioactive sources, which may lead to aggravation of breast lesions in young women. The dynamic optical breast scanning method uses infrared light to avoid the harm of X-rays to the human body. According to current research, doctors can use Dynamic Optical Breast Image (DOBI) to determine whether a patient has breast cancer. Studies have shown that convolutional neural networks (CNN) have higher detection accuracy in determining whether a patient has breast cancer. In this paper, we use an artificial intelligence neural network approach to analyze and process dynamic optical breast lesion images: we model the clinical lesion breast images in 3D, and use the VoxelMorph algorithm to segment the 3D images into 2D images; The time, space, location, and pathological trend curves in the image are analyzed and processed. We compared classification, sensitivity, and specific characteristics with the original dynamic breast lesion image scoring analysis system. The experimental results show that the accuracy is improved. At the same time, the problem of the original system's signature in the ROI area and leaf curve is solved. The use of CNN improves the analysis and processing speed, shortens the processing time, and increases the accuracy of the diagnostic reference from 83% to 90%.
VL  - 10
IS  - 2
ER  -

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Author Information

Kaiquan Chen

Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Zhide Li

Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

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Plain Text BibTeX RIS

APA Style

Kaiquan Chen, Zhide Li. (2022). Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images. International Journal of Biomedical Science and Engineering, 10(2), 38-43. https://doi.org/10.11648/j.ijbse.20221002.11

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ACS Style

Kaiquan Chen; Zhide Li. Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images. Int. J. Biomed. Sci. Eng. 2022, 10(2), 38-43. doi: 10.11648/j.ijbse.20221002.11

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AMA Style

Kaiquan Chen, Zhide Li. Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images. Int J Biomed Sci Eng. 2022;10(2):38-43. doi: 10.11648/j.ijbse.20221002.11

Copy | Download

@article{10.11648/j.ijbse.20221002.11,
  author = {Kaiquan Chen and Zhide Li},
  title = {Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {10},
  number = {2},
  pages = {38-43},
  doi = {10.11648/j.ijbse.20221002.11},
  url = {https://doi.org/10.11648/j.ijbse.20221002.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20221002.11},
  abstract = {Breast cancer is the most common cancer in women. At present, the methods of examining lesions are generally mammography, or B-scanning and other methods with certain radioactive sources, which may lead to aggravation of breast lesions in young women. The dynamic optical breast scanning method uses infrared light to avoid the harm of X-rays to the human body. According to current research, doctors can use Dynamic Optical Breast Image (DOBI) to determine whether a patient has breast cancer. Studies have shown that convolutional neural networks (CNN) have higher detection accuracy in determining whether a patient has breast cancer. In this paper, we use an artificial intelligence neural network approach to analyze and process dynamic optical breast lesion images: we model the clinical lesion breast images in 3D, and use the VoxelMorph algorithm to segment the 3D images into 2D images; The time, space, location, and pathological trend curves in the image are analyzed and processed. We compared classification, sensitivity, and specific characteristics with the original dynamic breast lesion image scoring analysis system. The experimental results show that the accuracy is improved. At the same time, the problem of the original system's signature in the ROI area and leaf curve is solved. The use of CNN improves the analysis and processing speed, shortens the processing time, and increases the accuracy of the diagnostic reference from 83% to 90%.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images
AU  - Kaiquan Chen
AU  - Zhide Li
Y1  - 2022/04/09
PY  - 2022
N1  - https://doi.org/10.11648/j.ijbse.20221002.11
DO  - 10.11648/j.ijbse.20221002.11
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 38
EP  - 43
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20221002.11
AB  - Breast cancer is the most common cancer in women. At present, the methods of examining lesions are generally mammography, or B-scanning and other methods with certain radioactive sources, which may lead to aggravation of breast lesions in young women. The dynamic optical breast scanning method uses infrared light to avoid the harm of X-rays to the human body. According to current research, doctors can use Dynamic Optical Breast Image (DOBI) to determine whether a patient has breast cancer. Studies have shown that convolutional neural networks (CNN) have higher detection accuracy in determining whether a patient has breast cancer. In this paper, we use an artificial intelligence neural network approach to analyze and process dynamic optical breast lesion images: we model the clinical lesion breast images in 3D, and use the VoxelMorph algorithm to segment the 3D images into 2D images; The time, space, location, and pathological trend curves in the image are analyzed and processed. We compared classification, sensitivity, and specific characteristics with the original dynamic breast lesion image scoring analysis system. The experimental results show that the accuracy is improved. At the same time, the problem of the original system's signature in the ROI area and leaf curve is solved. The use of CNN improves the analysis and processing speed, shortens the processing time, and increases the accuracy of the diagnostic reference from 83% to 90%.
VL  - 10
IS  - 2
ER  -

Copy | Download