Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning
AbstractCurrently, 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
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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.
Oliveira BL, Godinho D, O’Halloran M, Glavin M, Jones E, Conceição RC. Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning. Diagnostics. 2018; 8(2):36.Chicago/Turabian Style
Oliveira, Bárbara L.; Godinho, Daniela; O’Halloran, Martin; Glavin, Martin; Jones, Edward; Conceição, Raquel C. 2018. "Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning." Diagnostics 8, no. 2: 36.
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