Classification of Benign and Malignant Pulmonary Nodules Based on Deep Learning

Abstract

Lung cancer is one of the most common malignant tumors leading to death. Early diagnosis of pulmonary nodules is of great significance for reducing the mortality of the lung cancer patients. Recently, deep learning, i.e., the most potential technology in computer vision, has been achieving brilliant performance in a variety of medical image tasks. In this paper, we explore a novel pulmonary nodule classification method based on the recent ResNet and 2.5D view of CT images. The 2.5D view images are constructed by the 2D images that are re-sampled from the original 3D CT images through three different planes, i.e., coronal plane, sagittal plane and axial plane. Then, the recent proposed ResNet-50 network is adopted for feature extraction and classification of the pulmonary nodules in an end-to-end manner. The given method is evaluated on the public LIDC database, and the comparative experimental results show its effectiveness for the classification of benign and malignant pulmonary nodules.