The description of the datasets.

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.依托深度学习等先进技术开发的自动化工具，在提升计算机断层扫描（Computed Tomography, CT）影像中肺结节分类的准确率、最终降低肺癌死亡率方面展现出巨大应用前景。然而，由于不同成像模态所能提供的细节与清晰度存在差异，从CT影像中检测并分类肺结节往往颇具挑战。此外，现有的卷积神经网络（Convolutional Neural Network, CNN）在检测体积较小或位于肺部难以探查区域的结节时，往往表现欠佳。为此，本研究提出注意力金字塔池化网络（Attention Pyramid Pooling Network, APPN）用于肺结节的识别与分类。首先，采用名为VGG16的高性能特征提取器从CT影像中提取特征。随后，将注意力机制（Attention Mechanism）与金字塔池化模块（Pyramid Pooling Module）相结合，提出注意力初级金字塔模块，该模块可实现多尺度特征融合，并聚焦于肺结节分类所需的关键特征。最后，采用门控空间记忆技术（Gated Spatial Memory Technique）对全局特征进行解码，以提取更为精准的特征用于肺结节分类。在LIDC-IDRI数据集上开展的实验结果表明，APPN可实现高精度且高效的肺结节分类，其灵敏度达87.59%、特异度达90.46%、准确率达88.47%、阳性预测值达95.41%、阴性预测值达76.29%，受试者工作特征曲线下面积（Area Under Receiver Operating Characteristic Curve, AUC）为0.914。