Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules
This work addresses early detection of lung malignancies for medical diagnosis, but it appears incremental as it applies an existing neural network method to a specific domain with enhancements.
The paper tackled the problem of detecting pulmonary nodules from medical images by developing a 3D RCNN-based model integrated with 3D virtual modeling, resulting in a significantly improved recognition rate compared to conventional methods, as evaluated using FROC analysis on the LUNA16 dataset.
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.