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Development of automatic classification system for leukocyte images using Random Forest
Author(s) -
Tomiyama Shinnosuke,
SakataYanagimoto Mamiko,
Chiba Shigeru,
Aikawa Naoyuki
Publication year - 2018
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.12113
Subject(s) - support vector machine , random forest , pattern recognition (psychology) , artificial intelligence , feature vector , computer science , feature (linguistics) , classifier (uml) , feature extraction , one class classification , data mining , machine learning , philosophy , linguistics
Abstract Classifying leukocyte and examining their proportions is very important for disease examination and diagnosis. This examination needs the knowledge of experts and a lot of time. Therefore, many automatic leukocyte image classification algorithms have been proposed. There is a method to classify 13 types of blood cells using 1‐v‐1 support vector machine (SVM) in one of them. In the conventional method, leukocyte images are classified with the 26‐dimensional feature vectors. However, the classification accuracy is poor with these feature vectors in granulocytes in this method. In this article, we propose new feature vectors to improve the classification accuracy of blast cells with low classification accuracy among the leukocyte fractions. That is, we add two feature vectors in the proposed method. And we improve the accuracy of the whole by using a Random Forest for the classifier.