Helicobacter pylori -: Related gastric histology classification using support-vector-machine-based feature selection
Helicobacter pylori -: Related gastric histology classification using support-vector-machine-based feature selection
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DOI：
10.1109/titb.2007.913128
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发表时间：
2008-07-01
影响因子：
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通讯作者：
Popper, Mikulas
中科院分区：
文献类型：
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作者：
Huang, Chun-Rong;Chung, Pau-Choo;Popper, Mikulas
提出了一种基于支持向量机的序贯前向浮动选择(SFFS)的计算机辅助诊断系统，用于从内窥镜图像中诊断幽门螺杆菌(H.Pylori)的胃组织学。为了实现这一目标，从内窥镜图像中提取与临床症状相关的候选图像特征。对于这些候选特征，应用SFFS方法选择特征子集，对于不同的组织特征，在支持向量机下获得最佳的分类结果。通过使用从特征子集获得的分类器，实现了一个新的诊断系统，以向医生提供来自内窥镜图像的与幽门螺杆菌相关的组织学结果。
This study presents a computer-aided diagnosis system using sequential forward floating selection (SFFS) with support vector machine (SVM) to diagnose gastric histology of Heliobacter pylori (H. pylori) from endoscopic images. To achieve this goal, candidate image features associated with clinical symptoms ire extracted from endoscopic images. With these candidate features, the SFFS method is applied to select feature subsets, which perform the best classification results under SVM with respect to different histological features. By using the classifiers obtained from the feature subsets, a new diagnosis system is implemented to provide physicians with H. pylori-related histological results from endoscopic images.