关键词: 肺结节, 分类算法, 图像分割, 卷积神经网络, 深度学习

Abstract: The examination of the lung is a requisite part of the medical examination. There are quantities of cases in the medical examination, and each case contains plenty of cross-sectional CT images of the lung. While, all of these requires that professional doctors screen out the cases of lung nodules one by one, which not only cause tons of work, but also a possibility of inaccurate screening. Aiming at above problems, the Convolutional Neural Network（CNN） is introduced to screen out the CT images of lung nodules, and a classification algorithm based on CNN is proposed. The experimental results on the LIDC database show that, comparing with the widely used lenet-5 networks and traditional methods, the accuracy of classification is increased by 4 to 10 points by using self-defined convolutional neural network. AUC is 0.821?6, which is the largest of several classifiers. In brief, this method can distinguish CT images of the lung more accurately than other methods, and provides more objective reference for clinical diagnosis.

Key words: pulmonary nodule, classification algorithm, image segmentation, Convolutional Neural Network, deep learning