Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

Abstract

Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).

MICCAI 2017 accepted

References in corpus (1)

• Very Deep Convolutional Networks for Large-Scale Image Recognition

Cited by in corpus (5)

• DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
• DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
• Deep Learning for Automated Medical Image Analysis
• Automated pulmonary nodule detection using 3D deep convolutional neural networks
• An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction