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

Tuba Yilmaz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Article number530
JournalSensors
Volume20
Issue number2
DOIs
Publication statusPublished - Jan 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Funding

This work has received funding from the Scientific and Technological Research Council of Turkey under grant agreement 118S074 and the Istanbul Technical University under grant agreement 41554.

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu118S074
Istanbul Teknik Üniversitesi41554

    Keywords

    • Hepatic malignancies
    • In vivo dielectric properties
    • K-nearest neighbors (kNN)
    • Liver phantoms
    • Logistic regression (LR)
    • Machine learning
    • Random forests (RF)

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