Radar-Based Microwave Breast Imaging Using Neurocomputational Models

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dc.authorid0000-0003-3278-6071
dc.authorscopusid56436055700
dc.authorwosidF-7742-2015
dc.contributor.authorBiçer, Mustafa Berkan
dc.date.accessioned2023-05-11T13:54:41Z
dc.date.available2023-05-11T13:54:41Z
dc.date.issued2023
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractIn this study, neurocomputational models are proposed for the acquisition of radar-based microwave images of breast tumors using deep neural networks (DNNs) and convolutional neural networks (CNNs). The circular synthetic aperture radar (CSAR) technique for radar-based microwave imaging (MWI) was utilized to generate 1000 numerical simulations for randomly generated scenarios. The scenarios contain information such as the number, size, and location of tumors for each simulation. Then, a dataset of 1000 distinct simulations with complex values based on the scenarios was built. Consequently, a real-valued DNN (RV-DNN) with five hidden layers, a real-valued CNN (RV-CNN) with seven convolutional layers, and a real-valued combined model (RV-MWINet) consisting of CNN and U-Net sub-models were built and trained to generate the radar-based microwave images. While the proposed RV-DNN, RV-CNN, and RV-MWINet models are real-valued, the MWINet model is restructured with complex-valued layers (CV-MWINet), resulting in a total of four models. For the RV-DNN model, the training and test errors in terms of mean squared error (MSE) are found to be 103.400 and 96.395, respectively, whereas for the RV-CNN model, the training and test errors are obtained to be 45.283 and 153.818. Due to the fact that the RV-MWINet model is a combined U-Net model, the accuracy metric is analyzed. The proposed RV-MWINet model has training and testing accuracy of 0.9135 and 0.8635, whereas the CV-MWINet model has training and testing accuracy of 0.991 and 1.000, respectively. The peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM) metrics were also evaluated for the images generated by the proposed neurocomputational models. The generated images demonstrate that the proposed neurocomputational models can be successfully utilized for radar-based microwave imaging, especially for breast imaging.
dc.identifier.citationBicer, M. B. (2023). Radar-Based Microwave Breast Imaging Using Neurocomputational Models. Diagnostics (2075-4418), 13(5), 930. https://doi.org/10.3390/diagnostics13050930
dc.identifier.doi10.3390/diagnostics13050930
dc.identifier.endpage23en_US
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85149807886
dc.identifier.scopusqualityQ2
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13050930
dc.identifier.urihttps://hdl.handle.net/20.500.13099/121
dc.identifier.volume13en_US
dc.identifier.wosWOS:000947601900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBiçer, Mustafa Berkan
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectInverse Scattering
dc.subjectCircular Synthetic Aperture Radar (CSAR)
dc.subjectBreast Imaging
dc.subjectDeep Neural Nnetworks (DNNs)
dc.subjectConvolutional Neural Networks (CNNs
dc.titleRadar-Based Microwave Breast Imaging Using Neurocomputational Models
dc.typeArticle

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