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A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis

Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modali...

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Autores principales: Guo, Zicheng, Xie, Jiping, Wan, Yi, Zhang, Min, Qiao, Liang, Yu, Jiaxuan, Chen, Sijing, Li, Bingxin, Yao, Yongqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9743193/
https://www.ncbi.nlm.nih.gov/pubmed/36561500
http://dx.doi.org/10.1515/biol-2022-0517
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author Guo, Zicheng
Xie, Jiping
Wan, Yi
Zhang, Min
Qiao, Liang
Yu, Jiaxuan
Chen, Sijing
Li, Bingxin
Yao, Yongqiang
author_facet Guo, Zicheng
Xie, Jiping
Wan, Yi
Zhang, Min
Qiao, Liang
Yu, Jiaxuan
Chen, Sijing
Li, Bingxin
Yao, Yongqiang
author_sort Guo, Zicheng
collection PubMed
description Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.
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spelling pubmed-97431932022-12-21 A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis Guo, Zicheng Xie, Jiping Wan, Yi Zhang, Min Qiao, Liang Yu, Jiaxuan Chen, Sijing Li, Bingxin Yao, Yongqiang Open Life Sci Review Article Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities. De Gruyter 2022-12-09 /pmc/articles/PMC9743193/ /pubmed/36561500 http://dx.doi.org/10.1515/biol-2022-0517 Text en © 2022 the author(s), published by De Gruyter https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License.
spellingShingle Review Article
Guo, Zicheng
Xie, Jiping
Wan, Yi
Zhang, Min
Qiao, Liang
Yu, Jiaxuan
Chen, Sijing
Li, Bingxin
Yao, Yongqiang
A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
title A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
title_full A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
title_fullStr A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
title_full_unstemmed A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
title_short A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis
title_sort review of the current state of the computer-aided diagnosis (cad) systems for breast cancer diagnosis
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9743193/
https://www.ncbi.nlm.nih.gov/pubmed/36561500
http://dx.doi.org/10.1515/biol-2022-0517
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