Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease
Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease
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DOI：
10.1007/s10278-017-0028-9
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发表时间：
2018-08-01
影响因子：
4.4
通讯作者：
Lynch, David A.
中科院分区：
文献类型：
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作者：
Kim, Guk Bae;Jung, Kyu-Hwan;Lynch, David A.
本研究旨在比较间质性肺疾病（ILD）模式分类的浅层和深度学习。使用高分辨率计算机断层扫描图像，两名经验丰富的放射科医生标记了1200个感兴趣区域（ROIs），其中每个区域使用GE或西门子扫描仪采集600个ROIs，每组600个ROIs由100个ROIs组成，用于子区域，包括正常和五种局部肺部疾病模式（毛玻璃样浑浊、实变、网状浑浊、肺气肿和蜂窝状）。我们采用了卷积神经网络（CNN），它有六个可学习层，包括四个卷积层和两个全连接层。将分类结果与支持向量机（SVM）的浅层学习分类结果进行了比较。与SVM分类器相比，CNN分类器的准确性显著提高了6- 9%。随着卷积层的增加，CNN的分类准确率从81.27%提高到95.12%。特别是在表现出病理模糊的情况下，例如正常和肺气肿病例之间或蜂窝状和网状阴影病例之间，卷积层的增加大大降低了每个病例之间的错误分类率。总之，CNN分类器显示出比SVM分类器显著更高的准确性，并且结果暗示了特定ILD模式固有的结构特征。
This study aimed to compare shallow and deep learning of classifying the patterns of interstitial lung diseases (ILDs). Using high-resolution computed tomography images, two experienced radiologists marked 1200 regions of interest (ROIs), in which 600 ROIs were each acquired using a GE or Siemens scanner and each group of 600 ROIs consisted of 100 ROIs for subregions that included normal and five regional pulmonary disease patterns (ground-glass opacity, consolidation, reticular opacity, emphysema, and honeycombing). We employed the convolution neural network (CNN) with six learnable layers that consisted of four convolution layers and two fully connected layers. The classification results were compared with the results classified by a shallow learning of a support vector machine (SVM). The CNN classifier showed significantly better performance for accuracy compared with that of the SVM classifier by 6-9%. As the convolution layer increases, the classification accuracy of the CNN showed better performance from 81.27 to 95.12%. Especially in the cases showing pathological ambiguity such as between normal and emphysema cases or between honeycombing and reticular opacity cases, the increment of the convolution layer greatly drops the misclassification rate between each case. Conclusively, the CNN classifier showed significantly greater accuracy than the SVM classifier, and the results implied structural characteristics that are inherent to the specific ILD patterns.