School of Network Engineering, Zhoukou Normal University, Zhoukou, China.
College of Public Health, Zhengzhou University, Zhengzhou, China.
PLoS One. 2024 May 16;19(5):e0302641. doi: 10.1371/journal.pone.0302641. eCollection 2024.
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.
利用深度学习等先进技术开发自动化工具，有望提高计算机断层扫描（CT）成像中肺结节分类的准确性，从而降低肺癌死亡率。然而，从 CT 图像中检测和分类肺结节具有一定难度，因为不同的成像方式可能提供不同程度的细节和清晰度。此外，现有的卷积神经网络可能难以检测到小的或位于肺部难以检测区域的结节。因此，提出了注意金字塔池化网络（APPN）来识别和分类肺结节。首先，使用强大的特征提取器 vgg16 从 CT 图像中获取特征。然后，通过结合注意力机制和金字塔池化模块提出注意主金字塔模块，允许在不同尺度上融合特征，并关注对结节分类最重要的特征。最后，我们使用门控空间记忆技术对一般特征进行解码，能够提取更准确的特征来分类肺结节。在 LIDC-IDRI 数据集上的实验结果表明，APPN 能够实现高度准确和有效的肺结节分类，其敏感性为 87.59%，特异性为 90.46%，准确性为 88.47%，阳性预测值为 95.41%，阴性预测值为 76.29%，受试者工作特征曲线下面积为 0.914。
