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The examination of the lungs is an important part of the annual physical examination. There are hundreds or thousands of cases in the physical examination, and each case contains many lung cross-sectional CT images. These all require professional doctors to screen for cases with pulmonary nodules one by one, which is not only a heavy workload but also a possibility of incorrect screening. Aiming at the above problems, a Convolutional Neural Network (CNN) is introduced to screen out the CT images for pulmonary nodules, and a classification algorithm based on CNN is proposed. The experimental results in the LIDC database show that compared with the widely used lenet-5 network, traditional methods, and other deep learning models, the use of customized convolutional neural networks improves the classification accuracy. The AUC value is 0.821 6, which is also the highest among several classifiers. Compared with other methods, this method can more accurately identify CT images of the lungs and can provide a more objective reference for clinical diagnosis as it can be used for CAD systems. © 2020 ACM.
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