Khan, Rimsha, Masala, Giovanni Luca (2023) Detecting breast arterial calcifications in mammograms with transfer learning. Electronics, 12 (1). Article Number 231. E-ISSN 2079-9292. (doi:10.3390/electronics12010231) (KAR id:99769)
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Official URL: https://doi.org/10.3390/electronics12010231 |
Abstract
Cardiovascular diseases, which include all heart and circulatory diseases, are among the major death-causing diseases in women. Cardiovascular diseases are not subject to screening programs, and early detection can reduce their mortal effect. Recent studies have shown a strong association between severe Breast Arterial Calcifications and cardiovascular diseases. The aim of this study is to use the screening programs for breast cancer to detect the high severity of BACs and therefore to obtain indirect information about coronary diseases. Previous attempts in the literature on the detection of BACs from digital mammograms still need improvements to be used as a standalone technique. In this study, a dataset of mammograms with BACs is divided into 4 grades of severity, and this study aims to improve their classification through a transfer learning approach to overcome the need for a large dataset of training. The performances achieved in this study by using pre-trained models to detect four Breast Arterial Calcifications severity grades reached an accuracy of 94% during testing. Therefore, it is possible to benefit from the advantage of Deep Learning models to define a rapid marker of BACs along Brest Cancer screening programs.
Item Type: | Article |
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DOI/Identification number: | 10.3390/electronics12010231 |
Uncontrolled keywords: | Breast Arterial Calcifications; Cardiovascular Diseases; coronary artery disease; deep learning; transfer learning |
Subjects: |
Q Science > Q Science (General) > Q335 Artificial intelligence Q Science > QA Mathematics (inc Computing science) R Medicine R Medicine > R Medicine (General) > R858 Computer applications to medicine. Medical informatics. Medical information technology R Medicine > R Medicine (General) > R895 Medical physics. Medical radiology. Nuclear medicine |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Giovanni Masala |
Date Deposited: | 30 Jan 2023 12:01 UTC |
Last Modified: | 22 Nov 2023 17:07 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/99769 (The current URI for this page, for reference purposes) |
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