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

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Tarih
2020-01
Yazarlar
Yılmaz, Tuba
Süreli Yayın başlığı
Süreli Yayın ISSN
Cilt Başlığı
Yayınevi
MDPI
Özet
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.
Açıklama
Anahtar kelimeler
hepatic malignancies, in vivo dielectric properties, machine learning, k-nearest neighbors (kNN), logistic regression (LR), random forests (RF), liver phantoms
Alıntı
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