Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network
Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network
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DOI：
10.1016/j.cmpb.2019.05.020
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发表时间：
2019-08-01
影响因子：
6.1
通讯作者：
Liao, Wei-Chih
中科院分区：
文献类型：
--
作者：
Chen, Chia-Hung;Lee, Yan-Wei;Liao, Wei-Chih
背景和目的：在美国，肺癌是癌症死亡的主要原因。早期发现可以提高生存率。近年来，支气管内超声（EBUS）图像因其实时性、无辐射、性能较好，已被用于鉴别良恶性病变和指导经支气管穿刺。然而，诊断依赖于医生的主观判断。在以往的一些研究中，虽然利用EBUS图像的灰度图像纹理对肺部病变进行分类，但它属于半自动化系统，仍然需要专家先选择病变的一部分。因此，本研究的主要目的是利用卷积神经网络实现全自动化辅助。方法：首先，将EBUS图像调整为卷积神经网络（CNN）的输入大小。然后，训练数据被旋转和翻转。将之前使用ImageNet训练的模型参数转移到用于肺病变分类的CaffeNet中。然后，利用EBUS训练数据对CaffeNet的参数进行优化。从第7层全连通层提取4096维的特征，利用支持向量机（SVM）进行良恶性区分。本研究通过164例病例进行验证，其中良性56例，恶性108例。结果：实验结果表明，结合迁移学习的CNN特征分类方法优于传统的灰度共生矩阵（GLCM）特征分类方法。准确率85.4%(140/164)，灵敏度87.0%(94/108)，特异度82.1% (46/56)，ROC下面积0.8705。结论：从实验结果来看，利用CNN对EBUS图像进行诊断具有潜在的能力。(C) 2019 Elsevier B.V.版权所有
Background and objective: In the United States, lung cancer is the leading cause of cancer death. The survival rate could increase by early detection. In recent years, the endobronchial ultrasonography (EBUS) images have been utilized to differentiate between benign and malignant lesions and guide transbronchial needle aspiration because it is real-time, radiation-free and has better performance. However, the diagnosis depends on the subjective judgment from doctors. In some previous studies, which using the grayscale image textures of the EBUS images to classify the lung lesions but it belonged to semi-automated system which still need the experts to select a part of the lesion first. Therefore, the main purpose of this study was to achieve full automation assistance by using convolution neural network.Methods: First of all, the EBUS images resized to the input size of convolution neural network (CNN). And then, the training data were rotated and flipped. The parameters of the model trained with ImageNet previously were transferred to the CaffeNet used to classify the lung lesions. And then, the parameter of the CaffeNet was optimized by the EBUS training data. The features with 4096 dimension were extracted from the 7th fully connected layer and the support vector machine (SVM) was utilized to differentiate benign and malignant. This study was validated with 164 cases including 56 benign and 108 malignant.Results: According to the experiment results, applying the classification by the features from the CNN with transfer learning had better performance than the conventional method with gray level co-occurrence matrix (GLCM) features. The accuracy, sensitivity, specificity, and the area under ROC achieved 85.4% (140/164), 87.0% (94/108), 82.1% (46/56), and 0.8705, respectively.Conclusions: From the experiment results, it has potential ability to diagnose EBUS images with CNN. (C) 2019 Elsevier B.V. All rights reserved.