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Associative Classification Of Mammograms Using Weighted Rules

机译：乳腺X线照片的加权分类

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摘要

In this paper, we present a novel method for the classification of mammograms using a unique weighted association rule based classifier. Images are preprocessed to reveal regions of interest. Texture components are extracted from segmented parts of the image and discretized for rule discovery. Association rules are derived between various texture components extracted from segments of images and employed for classification based on their intra- and inter-class dependencies. These rules are then employed for the classification of a commonly used mammography dataset, and rigorous experimentation is performed to evaluate the rules' efficacy under different classification scenarios. The experimental results show that this method works well for such datasets, incurring accuracies as high as 89%, which surpasses the accuracy rates of other rule based classification techniques.
机译：在本文中，我们提出了一种基于唯一加权关联规则的乳腺X线照片分类方法。对图像进行预处理以显示感兴趣的区域。从图像的分割部分提取纹理分量，并离散化以进行规则发现。关联规则是在从图像片段中提取的各种纹理成分之间得出的，并根据它们在类内和类间的依赖性进行分类。然后，将这些规则用于常见的乳腺X线照片数据集的分类，并进行严格的实验以评估规则在不同分类情况下的功效。实验结果表明，该方法适用于此类数据集，准确率高达89％，超过了其他基于规则的分类技术的准确率。

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