Lung Nodule Classification using Deep Local-Global Networks

TL;DR

This paper introduces a novel deep learning approach combining local and global feature extraction for lung nodule classification, achieving state-of-the-art accuracy on the LIDC-IDRI dataset.

Contribution

The paper proposes a deep local-global network with residual and non-local blocks for improved lung nodule malignancy prediction, outperforming existing architectures.

Findings

Achieved AUC of 95.62% on LIDC-IDRI dataset.

Outperformed Densenet and Resnet with transfer learning.

Validated with 10-fold cross-validation.

Abstract

Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor. Methods: We propose to use Residual Blocks with a 3x3 kernel size for local feature extraction, and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps. Results: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1,018 computed tomography (CT) scans. We followed a rigorous procedure for experimental setup namely, 10-fold cross-validation and ignored the nodules that had been annotated by less than 3 radiologists. The proposed method achieved state-of-the-art results with AUC=95.62%, while significantly outperforming other baseline methods. Conclusions: Our proposed Deep Local-Global network has the capability to accurately extract both local and global features. Our new method outperforms state-of-the-art architecture including Densenet and Resnet with transfer learning.