Microwave Medical Diagnosis System With a Framework to Optimize the Antenna Configuration and Frequency of Operation Using Neural Networks

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dc.authoridYilmaz, Tuba/0000-0003-3052-2945|akduman, ibrahim/0000-0002-5769-6032
dc.authorwosidJafariFarmand, Aysa/O-2194-2018
dc.authorwosidYilmaz, Tuba/ABE-9932-2022
dc.contributor.authorJafarifarmand, Aysa
dc.contributor.authorYilmaz, Tuba
dc.contributor.authorAkduman, Ibrahim
dc.date.accessioned2024-07-18T20:47:28Z
dc.date.available2024-07-18T20:47:28Z
dc.date.issued2022
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractUsing artificial neural networks (NNs) in microwave medical diagnosis is recently of great interest in various problems such as early breast cancer detection, brain stroke, and leukemia monitoring. NNs facilitate the process by directly assessing the presence and properties of the tissues based on the scattered field values. Although the reported studies obtained successful results through the application of NNs to microwave diagnostic problems, they used large numbers of input data. The NN input, referred to as features, for microwave diagnosis is composed of scattered fields namely antenna transmission and reflections at the frequency of choice. Large input data increase both the number of required training samples and computational cost. Optimizing the number of antennas and frequency of operation is therefore critical to improving the performance of NN-based medical diagnosis. This work considers the correlations between the effects of different frequencies and receiver/transmitter (Rx/Tx) antennas separately in order to objectively reduce the number of features. Optimized feed-forward NNs are applied to detect the presence of object(s) with permittivity value above the predefined level within the solution domain. It is performed by designating various permittivity values to the internal object(s). Promising results were obtained by reducing the number of features approximately seven times.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [118S074]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 118S074.en_US
dc.identifier.doi10.1109/TMTT.2022.3210202
dc.identifier.endpage5104en_US
dc.identifier.issn0018-9480
dc.identifier.issn1557-9670
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85139875128en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage5095en_US
dc.identifier.urihttps://doi.org/10.1109/TMTT.2022.3210202
dc.identifier.urihttps://hdl.handle.net/11411/7800
dc.identifier.volume70en_US
dc.identifier.wosWOS:000869039600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Microwave Theory and Techniquesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMicrowave İmagingen_US
dc.subjectMicrowave Theory And Techniquesen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectMicrowave Antennasen_US
dc.subjectImagingen_US
dc.subjectPermittivityen_US
dc.subjectMicrowave İntegrated Circuitsen_US
dc.subjectArtificial Neural Networks (Nns)en_US
dc.subjectBreast Cancer Diagnosisen_US
dc.subjectElectromagnetic Scattered Fielden_US
dc.subjectMicrowave Medical Diagnosisen_US
dc.titleMicrowave Medical Diagnosis System With a Framework to Optimize the Antenna Configuration and Frequency of Operation Using Neural Networks
dc.typeArticle

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