Approaches to Automatic Classification, Detection and Segmentation of Breast Arterial Calcification Using Deep Learning.
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| Title: | Approaches to Automatic Classification, Detection and Segmentation of Breast Arterial Calcification Using Deep Learning. |
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| Authors: | Maguire, Dominic1 (AUTHOR) d.maguire3@edu.salford.ac.uk, Thompson, John D.2 (AUTHOR), Vadera, Sunil3 (AUTHOR), Szczepura, Katy1 (AUTHOR) |
| Source: | Expert Systems. Jun2025, Vol. 42 Issue 6, p1-20. 20p. |
| Subjects: | Object recognition (Computer vision), Arterial calcification, Calcifications of the breast, Feature extraction, Coronary artery disease |
| Abstract: | Objective: Cardiovascular disease (CVD) is the leading cause of premature death in the United Kingdom with one type, coronary artery disease, killing more than two times as many women as breast cancer. Recently, researchers have noted that breast arterial calcification (BAC), which is regularly observed as an incidental finding on mammograms, could be used to risk‐stratify women for CVD. However, identifying BAC is known to be a tedious, expensive and time‐consuming process. Thus, this paper investigates deep learning models for BAC classification, object detection and segmentation. Methodology: A data set, annotated under the guidance of two consultant radiologists, was created using data augmentation. This was used to evaluate several alternative deep learning models. Results: A modified ResNet22 classification network achieved a test accuracy of 80%, indicating that this method could be used as a flag for the presence or absence of BAC. We also used this network for feature extraction in a YOLOv4 BAC object detection network. Despite improving on a recent similar study, this latter network performed poorly with very low average precision scores at several thresholds. More promising was our DeepLabv3+‐based BAC segmentation network, which reached similar high global accuracy scores to three recent studies and a BFScore of over 70% specifically for BAC. It also performed satisfactorily on an unseen data set. Conclusions: These results show the potential for using classification and segmentation models as part of a pipeline for detecting BAC. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 185122822 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Approaches to Automatic Classification, Detection and Segmentation of Breast Arterial Calcification Using Deep Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Maguire%2C+Dominic%22">Maguire, Dominic</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> d.maguire3@edu.salford.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Thompson%2C+John+D%2E%22">Thompson, John D.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vadera%2C+Sunil%22">Vadera, Sunil</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Szczepura%2C+Katy%22">Szczepura, Katy</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems%22">Expert Systems</searchLink>. Jun2025, Vol. 42 Issue 6, p1-20. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Arterial+calcification%22">Arterial calcification</searchLink><br /><searchLink fieldCode="DE" term="%22Calcifications+of+the+breast%22">Calcifications of the breast</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Coronary+artery+disease%22">Coronary artery disease</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objective: Cardiovascular disease (CVD) is the leading cause of premature death in the United Kingdom with one type, coronary artery disease, killing more than two times as many women as breast cancer. Recently, researchers have noted that breast arterial calcification (BAC), which is regularly observed as an incidental finding on mammograms, could be used to risk‐stratify women for CVD. However, identifying BAC is known to be a tedious, expensive and time‐consuming process. Thus, this paper investigates deep learning models for BAC classification, object detection and segmentation. Methodology: A data set, annotated under the guidance of two consultant radiologists, was created using data augmentation. This was used to evaluate several alternative deep learning models. Results: A modified ResNet22 classification network achieved a test accuracy of 80%, indicating that this method could be used as a flag for the presence or absence of BAC. We also used this network for feature extraction in a YOLOv4 BAC object detection network. Despite improving on a recent similar study, this latter network performed poorly with very low average precision scores at several thresholds. More promising was our DeepLabv3+‐based BAC segmentation network, which reached similar high global accuracy scores to three recent studies and a BFScore of over 70% specifically for BAC. It also performed satisfactorily on an unseen data set. Conclusions: These results show the potential for using classification and segmentation models as part of a pipeline for detecting BAC. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/exsy.70069 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Arterial calcification Type: general – SubjectFull: Calcifications of the breast Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Coronary artery disease Type: general Titles: – TitleFull: Approaches to Automatic Classification, Detection and Segmentation of Breast Arterial Calcification Using Deep Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Maguire, Dominic – PersonEntity: Name: NameFull: Thompson, John D. – PersonEntity: Name: NameFull: Vadera, Sunil – PersonEntity: Name: NameFull: Szczepura, Katy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02664720 Numbering: – Type: volume Value: 42 – Type: issue Value: 6 Titles: – TitleFull: Expert Systems Type: main |
| ResultId | 1 |
