
Evaluation of Robust Classifier Algorithm for Tissue Classification under Various Noise Levels
Author(s) -
Youn Su Hyun,
Shin Ki Young,
Choi Ahnryul,
Mun Joung Hwan
Publication year - 2017
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.17.0116.0113
Subject(s) - classifier (uml) , pattern recognition (psychology) , artificial intelligence , computer science , statistical classification , algorithm
Ultrasonic surgical devices are routinely used for surgical procedures. The incision and coagulation of tissue generate a temperature of 40 °C–150 °C and depend on the controllable output power level of the surgical device. Recently, research on the classification of grasped tissues to automatically control the power level was published. However, this research did not consider the specific characteristics of the surgical device, tissue denaturalization, and so on. Therefore, this research proposes a robust algorithm that simulates noise to resemble real situations and classifies tissue using conventional classifier algorithms. In this research, the bioimpedance spectrum for six tissues (liver, large intestine, kidney, lung, muscle, and fat) is measured, and five classifier algorithms are used. A signal‐to‐noise ratio of additive white Gaussian noise diversifies the testing sets, and as a result, each classifier's performance exhibits a difference. The k ‐nearest neighbors algorithm shows the highest classification rate of 92.09% ( p < 0.01 ) and a standard deviation of 1.92%, which confirms high reproducibility.