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Open AccessArticle

Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning

1
Electrical and Electronic Engineering, National University of Ireland Galway, H91 TK33 Galway, Ireland
2
Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
3
Translational Medical Device Lab, National University of Ireland Galway, H91 TK33 Galway, Ireland
*
Author to whom correspondence should be addressed.
Diagnostics 2018, 8(2), 36; https://doi.org/10.3390/diagnostics8020036
Received: 14 April 2018 / Revised: 15 May 2018 / Accepted: 16 May 2018 / Published: 19 May 2018
Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation. View Full-Text
Keywords: machine learning; automated breast diagnosis; microwave imaging machine learning; automated breast diagnosis; microwave imaging
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Oliveira, B.L.; Godinho, D.; O’Halloran, M.; Glavin, M.; Jones, E.; Conceição, R.C. Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning. Diagnostics 2018, 8, 36.

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