{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:43:09Z","timestamp":1778859789220,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,29]],"date-time":"2022-05-29T00:00:00Z","timestamp":1653782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FESR-FSE 2014\/2020"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations.<\/jats:p>","DOI":"10.3390\/s22114122","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"4122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8028-2268","authenticated-orcid":false,"given":"Sandra","family":"Costanzo","sequence":"first","affiliation":[{"name":"Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, Universit\u00e0 della Calabria, 87036 Rende, Italy"},{"name":"Inter-University National Research Center on Interactions between Electromagnetic Fields and Biosystems (ICEmB), 16145 Genoa, Italy"},{"name":"National Research Council of Italy (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), 80124 Naples, Italy"},{"name":"Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), 43124 Parma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7726-582X","authenticated-orcid":false,"given":"Alexandra","family":"Flores","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, Universit\u00e0 della Calabria, 87036 Rende, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni","family":"Buonanno","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, Universit\u00e0 della Calabria, 87036 Rende, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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