Computer Assisted Diagnosis (CAD) is an effective method to detect lung cancer from computed tomography (CT) scans. The development of artificial neural network makes CAD more accurate in detecting pathological changes. Due to the complexity of the lung environment, the existing neural network training still requires large datasets, excessive time, and memory space. To meet the challenge, we analysis 3D volumes as serialized 2D slices and present a new neural network structure lightweight convolutional neural network (CNN)‐long short‐term memory (LSTM) for lung nodule classification. Our network contains two main components: (a) optimized lightweight CNN layers with tiny parameter space for extracting visual features of serialized 2D images, and (b) LSTM network for learning relevant information among 2D images. In all experiments, we compared the training results of several models and our model achieved an accuracy of 91.78% for lung nodule classification with an AUC of 93%. We used fewer samples and memory space to train the model, and we achieved faster convergence. Finally, we analyzed and discussed the feasibility of migrating this framework to mobile devices. The framework can also be applied to cope with the small amount of training data and the development of mobile health device in future.

中文翻译：

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基于轻量级级联卷积神经网络的肺结节序列化分类方法-长短期记忆

计算机辅助诊断 (CAD) 是一种通过计算机断层扫描 (CT) 扫描检测肺癌的有效方法。人工神经网络的发展使CAD对病理变化的检测更加准确。由于肺部环境的复杂性，现有的神经网络训练仍然需要大量的数据集、过多的时间和内存空间。为了迎接挑战，我们将 3D 体积分析为序列化的 2D 切片，并提出了一种新的神经网络结构轻量级卷积神经网络 (CNN)-长短期记忆 (LSTM) 用于肺结节分类。我们的网络包含两个主要组件：（a）优化的轻量级 CNN 层，具有微小的参数空间，用于提取序列化 2D 图像的视觉特征，以及（b）LSTM 网络，用于学习 2D 图像之间的相关信息。在所有实验中，我们比较了几种模型的训练结果，我们的模型对肺结节分类的准确率为 91.78%，AUC 为 93%。我们使用更少的样本和内存空间来训练模型，并且我们实现了更快的收敛。最后，我们分析和讨论了将该框架迁移到移动设备的可行性。该框架还可以应用于应对少量训练数据和未来移动健康设备的发展。