Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data.

Journal article


Rana, S., Dey, M., Loretoni, Riccardo, Duranti, Michele, Sani, Lorenzo, Vispa, Alessandro, Ghavami, M., Dudley, Sandra and Tiberi, Gianluigi (2021). Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data. Diagnostics. 11 (10). https://doi.org/diagnostics11101930
AuthorsRana, S., Dey, M., Loretoni, Riccardo, Duranti, Michele, Sani, Lorenzo, Vispa, Alessandro, Ghavami, M., Dudley, Sandra and Tiberi, Gianluigi
AbstractRecently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.
KeywordsMammoWave; breast lesion detection; machine learning
Year2021
JournalDiagnostics
Journal citation11 (10)
PublisherMDPI
ISSN2075-4418
Digital Object Identifier (DOI)https://doi.org/diagnostics11101930
https://doi.org/10.3390/diagnostics11101930
Web address (URL)https://www.mdpi.com/2075-4418/11/10/1930
Funder/ClientEuropean Union's Horizon 2020
Publication dates
Print18 Oct 2021
Online18 Oct 2021
Publication process dates
Deposited20 Oct 2021
Accepted13 Oct 2021
Publisher's version
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Open
Accepted author manuscript
File Access Level
Controlled
Licensehttps://creativecommons.org/licenses/by/4.0/
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