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

TL;DR

This paper presents a novel deep learning framework combining 2D and 3D convolutional neural networks for highly accurate pulmonary nodule detection in CT images, significantly improving detection performance.

Contribution

It introduces a deconvolutional extension to Faster R-CNN and a 3D DCNN for false positive reduction, achieving state-of-the-art results in nodule detection.

Findings

Achieved an average FROC-score of 0.891 on LUNA16

Ranked 1st in the LUNA16 challenge

Demonstrated superior detection accuracy over existing methods

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).