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Neural network-based approach for the classification of wireless-capsule endoscopic images

机译:基于神经网络的无线胶囊内窥镜图像分类方法

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摘要

The importance of computer-assisted diagnosis in\udendoscopy is to assist the physician in detecting the status of tissues by characterising the features from the endoscopic image. In this paper schemes have been developed to extract new texture features from the texture spectra in the chromatic and achromatic domains for a selected region of interest from each colour component histogram of images\udacquired by the new M2A Swallowable Imaging Capsule.\udThe concept of fusion of multiple classifiers dedicated to\udspecific feature parameters and the implementation of an\udadvanced intelligent scheme have been also adopted in this\udstudy. The high detection accuracy of the proposed systems\udprovides thus an indication that such intelligent schemes\udcould be used as a supplementary diagnostic tool in capsule\udendoscopy.
机译:子宫内窥镜检查中计算机辅助诊断的重要性在于通过表征内窥镜图像的特征来帮助医生检测组织的状态。在本文中,已开发出方案,从新的M2A可吞咽成像胶囊要求的图像的每个颜色分量直方图中,从感兴趣的选定区域的色域和非色域的纹理光谱中提取新的纹理特征。本研究还采用了专门针对特定特征参数的多个分类器,以及先进智能方案的实现。所提出的系统的高检测精度/因此提供了这样的指示:这种智能方案可以用作胶囊/膀胱镜检查中的辅助诊断工具。

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