Lyu Juan, Ling Sai Ho
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:686-689. doi: 10.1109/EMBC.2018.8512376.
Lung cancer is one of the four major cancers in the world. Accurate diagnosing of lung cancer in the early stage plays an important role to increase the survival rate. Computed Tomography (CT)is an effective method to help the doctor to detect the lung cancer. In this paper, we developed a multi-level convolutional neural network (ML-CNN)to investigate the problem of lung nodule malignancy classification. ML-CNN consists of three CNNs for extracting multi-scale features in lung nodule CT images. Furthermore, we flatten the output of the last pooling layer into a one-dimensional vector for every level and then concatenate them. This strategy can help to improve the performance of our model. The ML-CNN is applied to ternary classification of lung nodules (benign, indeterminate and malignant lung nodules). The experimental results show that our ML-CNN achieves 84.81% accuracy without any additional hand-craft preprocessing algorithm. It is also indicated that our model achieves the best result in ternary classification.
肺癌是全球四大癌症之一。早期准确诊断肺癌对提高生存率起着重要作用。计算机断层扫描（CT）是帮助医生检测肺癌的有效方法。在本文中，我们开发了一种多级卷积神经网络（ML-CNN）来研究肺结节恶性分类问题。ML-CNN由三个卷积神经网络组成，用于提取肺结节CT图像中的多尺度特征。此外，我们将每个层级最后池化层的输出展平为一维向量，然后将它们连接起来。这种策略有助于提高我们模型的性能。ML-CNN应用于肺结节的三元分类（良性、不确定和恶性肺结节）。实验结果表明，我们的ML-CNN在没有任何额外手工预处理算法的情况下达到了84.81%的准确率。这也表明我们的模型在三元分类中取得了最佳结果。
