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Computer-aided diagnosis of lung nodule classification between benign nodule primary lung cancer and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

机译：使用转移学习的深度卷积神经网络对不同图像大小的良性结节原发性肺癌和转移性肺癌之间的计算机辅助诊断

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We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.
机译：我们开发了一种用于在良性结节，原发性肺癌和转移性肺癌之间进行分类的计算机辅助诊断（CADx）方法，并评估了以下内容：（i）深层卷积神经网络（DCNN）对于三元分类的CADx的有用性与传统方法（手工成像功能加机器学习）相比，（ii）转移学习的有效性，以及（iii）图像大小作为DCNN输入的效果。在先前建立的数据库的1240例患者中，包括1236例患者的计算机断层扫描图像和临床信息。对于常规方法，通过使用支持向量机在三个正交平面上使用旋转不变的均匀模式局部二进制模式来执行CADx。对于DCNN方法，使用具有和不具有转移学习的VGG-16卷积神经网络对CADx进行评估，并通过随机搜索对DCNN方法进行超参数优化。对于传统方法，带转移学习的DCNN方法和不带转移学习的DCNN方法，CADx的最佳平均验证准确度分别为55.9％，68.0％和62.4％。对于56、112和224的图像大小，具有转移学习的DCNN的最佳平均验证准确性分别为60.7％，64.7％和68.0％。 DCNN优于传统的CADx方法，并且使用转移学习时DCNN的准确性得到了提高。此外，我们发现更大的图像尺寸作为DCNN的输入可以提高肺结节分类的准确性。

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