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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10453798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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