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The application of traditional machine learning and deep learning techniques in mammography: a review

Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients’ physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement...

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Autores principales: Gao, Ying’e, Lin, Jingjing, Zhou, Yuzhuo, Lin, Rongjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453798/
https://www.ncbi.nlm.nih.gov/pubmed/37637035
http://dx.doi.org/10.3389/fonc.2023.1213045
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author Gao, Ying’e
Lin, Jingjing
Zhou, Yuzhuo
Lin, Rongjin
author_facet Gao, Ying’e
Lin, Jingjing
Zhou, Yuzhuo
Lin, Rongjin
author_sort Gao, Ying’e
collection PubMed
description Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients’ physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients’ overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions.
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spelling pubmed-104537982023-08-26 The application of traditional machine learning and deep learning techniques in mammography: a review Gao, Ying’e Lin, Jingjing Zhou, Yuzhuo Lin, Rongjin Front Oncol Oncology Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients’ physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients’ overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions. Frontiers Media S.A. 2023-08-11 /pmc/articles/PMC10453798/ /pubmed/37637035 http://dx.doi.org/10.3389/fonc.2023.1213045 Text en Copyright © 2023 Gao, Lin, Zhou and Lin 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 Oncology
Gao, Ying’e
Lin, Jingjing
Zhou, Yuzhuo
Lin, Rongjin
The application of traditional machine learning and deep learning techniques in mammography: a review
title The application of traditional machine learning and deep learning techniques in mammography: a review
title_full The application of traditional machine learning and deep learning techniques in mammography: a review
title_fullStr The application of traditional machine learning and deep learning techniques in mammography: a review
title_full_unstemmed The application of traditional machine learning and deep learning techniques in mammography: a review
title_short The application of traditional machine learning and deep learning techniques in mammography: a review
title_sort application of traditional machine learning and deep learning techniques in mammography: a review
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453798/
https://www.ncbi.nlm.nih.gov/pubmed/37637035
http://dx.doi.org/10.3389/fonc.2023.1213045
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