一种基于分层原型的骨质疏松分类方法

Mebarkia Meriem, Meraoumia Abdallah, Houam Lotfi, Khemaissia Seddik
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引用次数: 1

摘要

医生对医学成像的广泛使用使得计算机成为成像和分析的必需品。已经开发了几种临床决策工具,通过骨密度测量和/或骨图像分析来评估骨质疏松症的风险。本研究的目的是开发一种基于分层原型的骨质疏松症分类方法。这种方法允许您以交互方式识别不同级别数据流的多模态分布和数据空间,您还可以在其中识别自组织和自开发层次结构的有意义的原型。决策过程以“最接近原型”原则为基础,清晰明了。该方法允许用户以基于原型的分层和易于解释的形式表示从数据中检索到的信息,是解决现实世界中大型和复杂问题的有吸引力的工具。
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A Hierarchical Prototype-Based Approach for Osteoporosis Classification
The widespread use of medical imaging by doctors has made computers a necessity for imaging and analysis. Several clinical decision-making tools have been developed to assess osteoporosis risk through bone density measurement and/or bone image analysis. The purpose of this study is to develop a hierarchical prototype-based approach for classification of osteoporosis. This approach allows you to interactively recognize the multimodal distribution and data space of different levels of data streaming, where you also identify meaningful prototypes for self-organization and self-development hierarchies. The decision-making process is based on the "closest prototype" principle and is clear. The proposed method allows users to present the information retrieved from the data in a hierarchical and easily interpretable form based on a prototype, and is an attractive tool for solving large and complex problems in the real world.
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