Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm

Saçlı B., Aydmalp C., Cansiz G., Joof S., Yilmaz T., Çayören M., ...Daha Fazla

COMPUTERS IN BIOLOGY AND MEDICINE, cilt.112, 2019 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 112
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.compbiomed.2019.103366
  • Dergi Adı: COMPUTERS IN BIOLOGY AND MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Dielectric properties of renal calculi, Kidney stone, Open-ended coaxial probe, Cole-Cole parameters, Classification of kidney stones, Machine learning, k-nearest neighbors, THERMOGRAVIMETRIC ANALYSIS, KIDNEY-STONES, URINARY, UROLITHIASIS, TISSUE
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

The proper management of renal lithiasis presents a challenge, with the recurrence 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 and 6 GHz with 100 MHz intervals. Cole-Cole parameters are fitted to the measured dielectric properties with the generalized Newton-Raphson method. The renal calculi types are classified based on their Cole-Cole parameters as calcium oxalate, cystine, or struvite. The classification is performed using k-nearest neighbors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.