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首页> 外文期刊>Journal of Digital Imaging >Comparative Performance Analysis of State-of-the-Art Classification Algorithms Applied to Lung Tissue Categorization
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Comparative Performance Analysis of State-of-the-Art Classification Algorithms Applied to Lung Tissue Categorization

机译：最新分类算法在肺组织分类中的比较性能分析

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摘要

In this paper, we compare five common classifier families in their ability to categorize six lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) and with healthy tissue. The evaluated classifiers are naive Bayes, k-nearest neighbor, J48 decision trees, multilayer perceptron, and support vector machines (SVM). The dataset used contains 843 regions of interest (ROI) of healthy and five pathologic lung tissue patterns identified by two radiologists at the University Hospitals of Geneva. Correlation of the feature space composed of 39 texture attributes is studied. A grid search for optimal parameters is carried out for each classifier family. Two complementary metrics are used to characterize the performances of classification. These are based on McNemar’s statistical tests and global accuracy. SVM reached best values for each metric and allowed a mean correct prediction rate of 88.3% with high class-specific precision on testing sets of 423 ROIs. Key words Quantitative image analysis - feature extraction - texture analysis - chest high-resolution CT - supervised learning - support vector machines
机译：在本文中，我们比较了五种常见的分类器系列，它们在患有间质性肺病（ILD）和健康组织的患者的高分辨率计算机断层扫描（HRCT）图像中对六种肺组织模式进行分类的能力。评估的分类器是朴素贝叶斯，k最近邻，J48决策树，多层感知器和支持向量机（SVM）。所使用的数据集包含843个健康的关注区域（ROI）和由日内瓦大学医院的两名放射科医生确定的五种病理肺组织模式。研究了由39个纹理属性组成的特征空间的相关性。对每个分类器系列进行最佳参数的网格搜索。使用两个互补的指标来表征分类的性能。这些是基于McNemar的统计测试和整体准确性。 SVM达到每个指标的最佳值，并在423个ROI的测试集上具有88.3％的平均正确预测率和特定于类的高精度。关键词定量图像分析特征提取纹理分析胸部高分辨率CT监督学习支持向量机

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