Pulmonary nodule detection of medical image based on deep learning

In order to solve the problem of low detection rate and false positive of lung CT image nodules in traditional computer-aided diagnosis system, a model for pulmonary nodule detection of CT images based on deep learning was proposed. According to the three-dimensional nature of the CT image of the lung, the 3D Faster R-CNN is used to extract the features and the candidate nodules are detected. Then the 3D convolutional neural network is used to remove the false positive nodules. The method is tested on the LUNA16 dataset, and is evaluated by the FROC method commonly used in the international medical imaging field. The sensitivity index under different false-positive ratios is counted, and the average FROC value is 82.8%, which is compared with the traditional diagnosis and treatment method. The rate has improved significantly. This model can be used to assist doctors in the diagnosis of pulmonary nodules, and has certain clinical application value.