  The 3D simulation model of the lung was established by using the
reconstruction method. A computer aided pulmonary nodule detection model was
constructed. The process iterates over the images to refine the lung nodule
recognition model based on neural networks. It is integrated with 3D virtual
modeling technology to improve the interactivity of the system, so as to
achieve intelligent recognition of lung nodules. A 3D RCNN (Region-based
Convolutional Neural Network) was utilized for feature extraction and nodule
identification. The LUNA16 large sample database was used as the research
dataset. FROC (Free-response Receiver Operating Characteristic) analysis was
applied to evaluate the model, calculating sensitivity at various false
positive rates to derive the average FROC. Compared with conventional
diagnostic methods, the recognition rate was significantly improved. This
technique facilitates the detection of pulmonary abnormalities at an initial
phase, which holds immense value for the prompt diagnosis of lung malignancies.
