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      Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data

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          Abstract

          Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy.

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          Cross-Validation of Regression Models

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            A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries.

            The development of microwave breast cancer detection and treatment techniques has been driven by reports of substantial contrast in the dielectric properties of malignant and normal breast tissues. However, definitive knowledge of the dielectric properties of normal and diseased breast tissues at microwave frequencies has been limited by gaps and discrepancies across previously published studies. To address these issues, we conducted a large-scale study to experimentally determine the ultrawideband microwave dielectric properties of a variety of normal, malignant and benign breast tissues, measured from 0.5 to 20 GHz using a precision open-ended coaxial probe. Previously, we reported the dielectric properties of normal breast tissue samples obtained from reduction surgeries. Here, we report the dielectric properties of normal (adipose, glandular and fibroconnective), malignant (invasive and non-invasive ductal and lobular carcinomas) and benign (fibroadenomas and cysts) breast tissue samples obtained from cancer surgeries. We fit a one-pole Cole-Cole model to the complex permittivity data set of each characterized sample. Our analyses show that the contrast in the microwave-frequency dielectric properties between malignant and normal adipose-dominated tissues in the breast is considerable, as large as 10:1, while the contrast in the microwave-frequency dielectric properties between malignant and normal glandular/fibroconnective tissues in the breast is no more than about 10%.
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              A large-scale study of the ultrawideband microwave dielectric properties of normal breast tissue obtained from reduction surgeries.

              The efficacy of emerging microwave breast cancer detection and treatment techniques will depend, in part, on the dielectric properties of normal breast tissue. However, knowledge of these properties at microwave frequencies has been limited due to gaps and discrepancies in previously reported small-scale studies. To address these issues, we experimentally characterized the wideband microwave-frequency dielectric properties of a large number of normal breast tissue samples obtained from breast reduction surgeries at the University of Wisconsin and University of Calgary hospitals. The dielectric spectroscopy measurements were conducted from 0.5 to 20 GHz using a precision open-ended coaxial probe. The tissue composition within the probe's sensing region was quantified in terms of percentages of adipose, fibroconnective and glandular tissues. We fit a one-pole Cole-Cole model to the complex permittivity data set obtained for each sample and determined median Cole-Cole parameters for three groups of normal breast tissues, categorized by adipose tissue content (0-30%, 31-84% and 85-100%). Our analysis of the dielectric properties data for 354 tissue samples reveals that there is a large variation in the dielectric properties of normal breast tissue due to substantial tissue heterogeneity. We observed no statistically significant difference between the within-patient and between-patient variability in the dielectric properties.
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                Author and article information

                Contributors
                ranas9@lsbu.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                19 July 2019
                19 July 2019
                2019
                : 9
                : 10510
                Affiliations
                [1 ]ISNI 0000 0001 2112 2291, GRID grid.4756.0, Division of Electrical and Electronic Engineering, , School of Engineering, London South Bank University, ; London, United Kingdom
                [2 ]ISNI 0000 0004 1757 3630, GRID grid.9027.c, UBT Srl, , Spin Off of the University of Perugia, ; Perugia, Italy
                [3 ]Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy
                Author information
                http://orcid.org/0000-0002-8014-8122
                http://orcid.org/0000-0002-6862-7032
                http://orcid.org/0000-0001-8787-1295
                Article
                46974
                10.1038/s41598-019-46974-3
                6642213
                31324863
                4411c6f0-1cad-429c-883c-a6fc019dc0cf
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 September 2018
                : 4 July 2019
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                breast cancer,cancer imaging,biomedical engineering
                Uncategorized
                breast cancer, cancer imaging, biomedical engineering

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