Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 35, 26 April 2024
Open
Access | Article
Analysis of the advantages and sustainability of digital breast tomosynthesis
* Author to whom correspondence should be addressed.
Abstract
Digital breast tomosynthesis (DBT) plays an important role in medical imaging, especially in breast cancer screening and diagnosis. This article delves into the distinctive features of DBT, which surpass traditional 2D mammography in both sensitivity and specificity, resulting in clearer and more accurate breast imaging, thus improving breast cancer's detection rate and diagnostic accuracy. Despite these advantages, DBT faces limitations, including heightened radiation exposure, increased cost, and image processing and interpretation complexities.This article, drawing from theoretical frameworks and case studies, uncovers the potential of emerging technologies like deep learning to improve the reconstruction of two-dimensional images, bolster the progression of DBT, and drive advancements in breast screening technology. Through the combination of deep learning technology and DBT 3D data, future research can be dedicated to generating more accurate 2D images, thus further improving the efficiency and accuracy of breast cancer screening. Meanwhile, the introduction of the DCGNN model is anticipated to revolutionize deep feature extraction specifically for DBT-s2D image data, potentially leading to significant breakthroughs in this field.
Keywords
DBT, Breast Screening, 2D Images, Deep Learning, Convolutional Models
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-395-1
- ISBN (Online)
- 978-1-83558-396-8
- Published Date
- 26 April 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/35/20240944
- Copyright
- 26 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited