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Diagnosing Breast Cancer with Microwave Technology: remaining challenges and potential solutions with machine learning.


ABSTRACT: 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.

SUBMITTER: Oliveira BL 

PROVIDER: S-EPMC6023429 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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Diagnosing Breast Cancer with Microwave Technology: remaining challenges and potential solutions with machine learning.

Oliveira Bárbara L BL   Godinho Daniela D   O'Halloran Martin M   Glavin Martin M   Jones Edward E   Conceição Raquel C RC  

Diagnostics (Basel, Switzerland) 20180519 2


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  ...[more]

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