Gastroesophageal Reflux Disease Diagnosis Using Hierarchical Heterogeneous Descriptor Fusion Support Vector Machine

Chun Rong Huang, Yan Ting Chen, Wei Ying Chen, Hsiu Chi Cheng, Bor Shyang Sheu

研究成果: Article同行評審

17 引文 斯高帕斯(Scopus)

摘要

A new computer-aided diagnosis method is proposed to diagnose the gastroesophageal reflux disease (GERD) from endoscopic images of the esophageal-gastric junction. To avoid the interferences of different endoscope devices and automatic camera white balance adjustment, heterogeneous descriptors computed from heterogeneous color models are used to represent endoscopic images. Instead of concatenating these descriptors to a super vector, a hierarchical heterogeneous descriptor fusion support vector machine (HHDF-SVM) framework is proposed to simultaneously apply heterogeneous descriptors for GERD diagnosis and overcome the curse of dimensionality problem. During validation, heterogeneous descriptors are extracted from test endoscopic images at first. The classification result is obtained by using HHDF-SVM with heterogeneous descriptors. As shown in the experiments, our method can automatically diagnose GERD without any manual selection of region of interest and achieve better accuracy compared to states-of-the-art methods.

原文English
文章編號7182761
頁(從 - 到)588-599
頁數12
期刊IEEE Transactions on Biomedical Engineering
63
發行號3
DOIs
出版狀態Published - 2016 3月

All Science Journal Classification (ASJC) codes

  • 生物醫學工程

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