Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules

TL;DR

This paper presents a deep learning-based 3D neural network model for pulmonary nodule detection, integrating 3D modeling and virtual technology to enhance accuracy and early diagnosis of lung cancer.

Contribution

It introduces a novel 3D RCNN model combined with virtual modeling for improved pulmonary nodule recognition, validated on the LUNA16 dataset.

Findings

Recognition rate significantly improved over traditional methods

Model achieved high sensitivity at various false positive rates

Facilitates early detection of lung malignancies

Abstract

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.