# Lung Nodule Classification using Deep Local-Global Networks

arXiv: https://arxiv.org/abs/1904.10126 · HTML: https://mlsanity.com/paper/1904.10126/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement](https://mlsanity.com/paper/2304.00257/) (2023)
- [The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification](https://mlsanity.com/paper/2108.05386/) (2021)
- [Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis](https://mlsanity.com/paper/2206.13608/) (2022)
- [3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis](https://mlsanity.com/paper/2207.13830/) (2022)
- [Evaluating Adversarial Robustness of Low dose CT Recovery](https://mlsanity.com/paper/2402.11557/) (2024)
- [Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation](https://mlsanity.com/paper/2102.10919/) (2021)
- [MERA: Multimodal and Multiscale Self-Explanatory Model with Considerably Reduced Annotation for Lung Nodule Diagnosis](https://mlsanity.com/paper/2504.19357/) (2025)

## Abstract (arXiv)

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.