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Comparison of Supervised Learning Algorithms for RF-Based Breast Cancer Detection

Nayak, Sudarshan and Gope, Dipanjan (2017) Comparison of Supervised Learning Algorithms for RF-Based Breast Cancer Detection. In: International Workshop on Computing and Electromagnetics (CEM), JUN 21-24, 2017, Barcelona, SPAIN, pp. 13-14.

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Official URL: http://dx.doi.org/ 10.1109/CEM.2017.7991863

Abstract

Three-dimensional imaging based on radio frequency that exploits the contrast in dielectric properties of tissues may be used as a low-cost, non-invasive and non-ionizing methodology for breast cancer detection. This paper demonstrates the use of various supervised machine learning algorithms in classification of breast tissues into less-dense fatty and dense fibroglandular or malignant classes from the measured scattered electric field data obtained through antennas placed around the breast tissue. A comparison on the performance of these algorithms are also presented. Such a classification step may be followed by a quantitative non-linear optimization scheme to obtain a more precise reconstruction of the tissue profile.

Item Type: Conference Paper
Additional Information: Copy right for the article belong to IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Depositing User: Id for Latest eprints
Date Deposited: 08 Mar 2018 19:04
Last Modified: 06 Nov 2018 12:25
URI: http://eprints.iisc.ac.in/id/eprint/59145

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