Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest challenge in medical image classification with the help of CNN is the difficulty of acquiring enough samples, and overfitting is a common problem when there are not enough images for training. Transfer learning has been verified as reasonable in dealing with such problems with an acceptable loss value. We use the classic LeNet-5 model to classify pulmonary nodules of thoracic CT images, including benign and malignant pulmonary nodules, and different malignancies of the malignant nodules. The CT images are obtained from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) where both pulmonary nodule scanning and nodule annotations are available. These images are labeled and stored in a medical images knowledge base (KB), which is designed and implemented in our previous work. We implement the 10-folder cross validation (CV) to testify the robustness of the classification model we trained. The result demonstrates that the transfer learning of the LeNet-5 is good for classifying pulmonary nodules of thoracic CT images, and the average values of Top-1 accuracy are 97.041% and 96.685% respectively. We believe that our work is beneficial and has potential for practical diagnosis of lung nodules.

中文翻译：

---

使用迁移学习对胸部 CT 图像肺结节进行计算机辅助诊断 (CAD)。

计算机辅助诊断（CAD）已广泛应用于医学图像处理。我们最近再次尝试使用卷积神经网络（CNN）对胸部 CT 图像的肺部结节进行分类。借助CNN进行医学图像分类的最大挑战是难以获取足够的样本，当没有足够的图像进行训练时，过度拟合是一个常见问题。迁移学习已被证明在处理此类问题时是合理的，且损失值可接受。我们使用经典的LeNet-5模型对胸部CT图像的肺结节进行分类，包括良性和恶性肺结节，以及恶性结节的不同恶性程度。CT 图像从肺部图像数据库联盟和图像数据库资源倡议 (LIDC-IDRI) 获得，其中提供肺结节扫描和结节注释。这些图像被标记并存储在我们之前的工作中设计和实现的医学图像知识库（KB）中。我们实施 10 文件夹交叉验证（CV）来验证我们训练的分类模型的稳健性。结果表明，LeNet-5的迁移学习对于胸部CT图像的肺部结节分类效果良好，Top-1准确率的平均值分别为97.041%和96.685%。我们相信我们的工作是有益的，并且具有对肺结节进行实际诊断的潜力。