Research on Computer Aided Detection and Segmentation of Pulmonary Nodules Based on Deep Learning and Generative Adversarial Networks

Pulmonary tuberculosis assisted diagnosis technology mainly faces the problems of poor resistance to overfitting due to the small training dataset and the low accuracy of lung tissue interference detection. This paper mainly solves the current problems from two aspects. First, this paper uses the RetinaNet[l] network structure combined with the DenseNet [2] feature extraction network to perform image detection on lung CT targets. Second, this paper enhance the training dataset by increasing the size of the dataset with the method of generative adversarial network to reduce the phenomena such as overfitting in experimental results. After testing on the existing dataset, it was found that the accuracy rate of target detection based on the improved model is 87.32%, which is 70.1% on the original model. Besides, the accuracy rate can be increased to 90.1% by the enhancement of the dataset using generative adversarial network.