A Wavelet Coefficient-Based Convolutional Neural Network for Histological Classification of Lung Cancer in CT images
A Wavelet Coefficient-Based Convolutional Neural Network for Histological Classification of Lung Cancer in CT images
复制标题
基于小波系数的卷积神经网络对 CT 图像中肺癌的组织学分类
DOI：
10.11318/mii.36.64
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
蔡 篤儀
中科院分区：
文献类型：
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作者：
松山 江里;李 鎔範;高橋 規之;蔡 篤儀
近年来，卷积神经网络（CNN）已被开发用于医学成像研究领域，并已成功地显示出其在图像分类和检测方面的能力。在本文中，我们使用了CNN结合小波变换的方法进行组织学分类的数据集的548肺CT图像分为5类，如肺腺癌，肺鳞状细胞癌，转移性肺癌，潜在的肺癌和正常。常用的CNN和所提出的方法之间的主要区别在于，我们使用第一级的冗余小波系数作为CNN的输入，而不是使用原始图像。该方法的一个主要优点是不需要预先从图像中提取感兴趣区域。整个图像的小波系数被用作CNN的输入。我们将所提出的方法的分类性能与现有的CNN分类器和基于CNN的支持向量机分类器进行比较。实验结果表明，该方法可以达到最高的91.7%的整体准确率，并展示了潜在的用于肺部疾病的CT图像分类。
In recent years, convolutional neural networks (CNNs) have been exploited in medical imaging research field and have successfully shown their ability in image classification and detection. In this paper we used a CNN combined with a wavelet transform approach for histologically classifying a dataset of 548 lung CT images into 5 categories, eg lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, potential lung cancer and normal. The main difference between the commonly-used CNNs and the presented method is that we use redundant wavelet coefficients at level 1 as inputs to the CNN instead of using original images. One of the major advantages of the proposed method is that it is no need to extract the regions of interest from images in advance. The wavelet coefficients of the entire image are used as inputs to the CNN. We compare the classification performance of the proposed method to that of an existing CNN classifier and a CNN-based support vector machine classifier. The experimental results show that the proposed method can achieve the highest overall accuracy of 91.7% and demonstrate the potential for use in classification of lung diseases in CT images.