期刊
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
卷 37, 期 3, 页码 357-364出版社
ELSEVIER
DOI: 10.1016/j.bbe.2017.04.005
关键词
Ensemble of classifiers; Feature selection; Fuhrman grading; Wavelets; SVM; Random forest
The paper presents an improved system to recognition of Fuhrman grading in clear-cell renal carcinoma using an ensemble of classifiers. The novelty of solution includes the segmentation applying wavelet transformation in preprocessing stage, application of few selection methods for feature generation and using the ensemble of classifiers in final recognition step. The wavelet transformation is a very efficient tool for image de-noising and enhancing the edges of cell nuclei. The important distinction to other approaches is that diagnostic features of nuclei, based on the texture, geometry, color and histogram, are selected by using few methods, each relying on different mechanism of selection. These different sets of features have enabled creating the ensemble of classifiers based on the support vector machine and random forest, both cooperating with them. Such approach has led to the significant increase of the quality factors in comparison to the best existing results: sensitivity the average of this solution 94.3% compared to 91.5%) and specificity (the average 98.6% compared to 97.5%. (C) 2017 Published by Elsevier B.V. on behalf of Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据