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首页> 外文会议>International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology;ECTI-CON 2009 >DIMAR - Discovering interesting medical association rules form MRI scans
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DIMAR - Discovering interesting medical association rules form MRI scans

机译：DIMAR-通过MRI扫描发现有趣的医学关联规​​则

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Data mining is an expanding research frontier that provides numerous efficient and scalable methods to extract patterns of interest in datasets. In this paper , Computer Aided Diagnosis ( CAD ) is applied to brain MRI image processing. Four features based on texture as proposed by Harlick are extracted and stored in a transactional database. The system is then trained with the proposed efficient associative classifier. The existing CBA algorithm was extended to select only essential rules which help diagnosis of abnormal MRI of the brain. Our work is optimized in the sense it combines feature selection and discretization thereby reducing the mining complexity. The results showed higher sensitivity ( upto 98% ) and accuracy ( upto 97% ) allowing us to claim that association rules can effectively aid in the diagnosing task.
机译：数据挖掘是一个不断扩展的研究前沿，它提供了许多有效且可扩展的方法来提取数据集中感兴趣的模式。本文将计算机辅助诊断（CAD）技术应用于脑部MRI图像处理。提取Harlick提出的基于纹理的四个特征，并将其存储在事务数据库中。然后，使用建议的有效关联分类器对系统进行训练。现有的CBA算法已扩展为仅选择有助于诊断脑部MRI异常的基本规则。在结合特征选择和离散化的意义上优化了我们的工作，从而降低了挖掘的复杂性。结果显示出更高的灵敏度（高达98％）和准确性（高达97％），使我们可以断言关联规则可以有效地协助诊断任务。

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