Multiclass classification of hepatic anomalies with dielectric properties: From phantom materials to rat hepatic tissues

dc.contributor.author Yılmaz, Tuba
dc.contributor.department Elektronik ve Haberleşme Mühendisliği tr_TR
dc.contributor.department Electronics and Communication Engineering tr_TR
dc.date.accessioned 2020-11-26T07:20:02Z
dc.date.available 2020-11-26T07:20:02Z
dc.date.issued 2020-01
dc.description.abstract Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this technology, there is a need to decrease the measurement error which is reported to be more than 30% for an in vivo measurement setting. This work investigates the machine learning (ML) algorithms’ ability to decrease the measurement error of open-ended coaxial probe techniques to enable tissue characterization devices. To explore the potential of this technique as a tissue characterization device, performances of multiclass ML algorithms on collected in vivo rat hepatic tissue and phantom dielectric property data were evaluated. Phantoms were used for investigating the potential of proliferating the data set due to difficulty of in vivo data collection from tissues. The dielectric property measurements were collected from 16 rats with hepatic anomalies, 8 rats with healthy hepatic tissues, and in house phantoms. Three ML algorithms, k-nearest neighbors (kNN), logistic regression (LR), and random forests (RF) were used to classify the collected data. The best performance for the classification of hepatic tissues was obtained with 76% accuracy using the LR algorithm. The LR algorithm performed classification with over 98% accuracy within the phantom data and the model generalized to in vivo dielectric property data with 48% accuracy. These findings indicate first, linear models, such as logistic regression, perform better on dielectric property data sets. Second, ML models fitted to the data collected from phantom materials can partly generalize to in vivo dielectric property data due to the discrepancy between dielectric property variability. tr_TR
dc.identifier.citation Yilmaz, T. (2020). Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues. Sensors (14248220), 20(2), 1–13. https://doi.org/10.3390/s20020530 tr_TR
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/11527/18871
dc.language.iso en tr_TR
dc.publisher MDPI tr_TR
dc.subject hepatic malignancies tr_TR
dc.subject in vivo dielectric properties tr_TR
dc.subject machine learning tr_TR
dc.subject k-nearest neighbors (kNN) tr_TR
dc.subject logistic regression (LR) tr_TR
dc.subject random forests (RF) tr_TR
dc.subject liver phantoms tr_TR
dc.title Multiclass classification of hepatic anomalies with dielectric properties: From phantom materials to rat hepatic tissues tr_TR
dc.type Article tr_TR
dc.type Makale tr_TR
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