Abstract
Breast Microwave Imaging (BMI) has emerged as a competitive and potentially disruptive alternative to conventional breast cancer screening techniques owing to its desirable features and improved detection rate. In this paper, we apply various artificial intelligence, and deep learning approaches for automatic breast tumor detection, classification and localization in an open-source experimental BreastCare dataset obtained using our pre-clinical, portable and cost-effective BMI system. We compare the effectiveness of various cutting-edge machine-learning detection algorithms to assess the usefulness of the obtained data-set. Also, we present a deep learning framework that outperforms state-of-the-art microwave imaging methods and ML algorithms for tumor detection, localization, and characterization. The proposed framework gives promising results using our BMI system's measured reflection coefficients (S11). This work shows the potential advantages of applying cutting-edge deep learning algorithms in practical BMI systems.
Original language | English (US) |
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Title of host publication | ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665451093 |
DOIs | |
State | Published - 2023 |
Event | 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States Duration: May 21 2023 → May 25 2023 |
Publication series
Name | Proceedings - IEEE International Symposium on Circuits and Systems |
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Volume | 2023-May |
ISSN (Print) | 0271-4310 |
Conference
Conference | 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 |
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Country/Territory | United States |
City | Monterey |
Period | 05/21/23 → 05/25/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Convolutional Neural Networks (CNN)
- Deep learning (DL)
- Deep Neural Networks (DNN)
- localization
- Machine learning (ML)
- Microwave Imaging (MWI)
- Residual Neural Networks (ResNet)
ASJC Scopus subject areas
- Electrical and Electronic Engineering