Enhancing Pulmonary Nodule Detection Rate Using 3D Convolutional Neural Networks With Optical Flow Frame Insertion Technique

The lung nodule detection technology plays a vital role in the diagnosis and treatment of early lung cancer. Deep learning is currently one of the main technologies applied in computer vision related fields. Therefore, this topic is to combine deep learning and lung nodule detection technology. Different from some research methods, we improved from two-dimensional space to three- dimensional space and applied it to lung nodule detection. After statistically analyzing the lung CT data, we find that the lung CT data has inconsistent scales on the vertical axis. Aiming at the problem of lung CT data, this article is based on the deep learning method. First, the lung interpolation network based on voxel flow is used to achieve the same scale, and then the lung nodule detection network based on three-dimensional convolution is used to complete the detection of lung nodules. The entire network combines U- Net-like and RPN-like network structures. Through data slice input, it avoids the limitation of the display memory of the computing platform. The network structure also introduces prior knowledge of the coordinate information of the input slices to improve the classification accuracy of lung nodules. The experimental results show that the lung slice data of consistent scale is achieved through interpolation, and then through the three-dimensional convolution of the lung nodule detection network, the state- of-the-art detection effect is achieved. Because of the introduction of the interpolation network, the time- consuming has increased. Of course, the overall speed stillmeets the actual use value.