Please use this identifier to cite or link to this item: http://hdl.handle.net/11527/18055
Title: Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm
Authors: Saçlı, Banu
Aydınalp, Cemanur
Cansız, Gökhan
joof, Sulayman
Yılmaz, Tuba
Çayören, Mehmet
Önal, Bülent
Akduman, İbrahim
Elektronik ve Haberleşme Mühendisliği
Electronics and Communication Engineering
Keywords: Dielectric properties of renal calculi
Kidney stone
Open-ended coaxial probe
Cole–Cole parameters
Classification of kidney stones
Machine learning
k-nearest neighbors
Issue Date: 2019
Publisher: Elsevier
Citation: Saçlı, B., Aydınalp, C., Cansız, G., Joof, S., Yilmaz, T., Çayören, M., … Akduman, I. (2019). Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm. Computers in Biology and Medicine, 112, 103366. https://doi.org/10.1016/j.compbiomed.2019.103366
Abstract: The proper management of renal lithiasis presents a challenge, with the recur- rence rate of the disease being as high as 46%. To prevent recurrence, the first step is the accurate categorization of the discarded renal calculi. Currently, the discarded renal calculi type is determined with the X-ray powder diffraction method which requires a cumbersome sample preparation. This work presents a new approach that can enable fast and accurate classification of discarded renal calculi with minimal sample preparation requirements. To do so, first, the measurements of the dielectric properties of naturally formed renal calculi are collected with the open-ended contact probe technique between 500 MHz to 6 GHz with 100 MHz intervals. Cole–Cole parameters are fitted to the measured dielectric properties with the generalized Newton–Raphson method. The re- nal calculi types are classified based on their Cole–Cole parameters as calcium oxalate, cystine, or struvite. The classification is performed using nearest neigh- bors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.
URI: https://www.sciencedirect.com/science/article/pii/S0010482519302434
http://hdl.handle.net/11527/18055
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