Papers › Lung Nodule Classification using Deep Local-Global Networks

Lung Nodule Classification using Deep Local-Global Networks

23 Apr 2019arXiv:1904.10126archive 2025-07-28

Mundher Al-Shabi, Boon Leong Lan, Wai Yee Chan, Kwan-Hoong Ng, Maxine Tan

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.

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mundher/local-global mentioned on GitHubpytorch report

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Tasks

Classification Computed Tomography (CT)General Classification Lung Nodule Classification Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Lung Nodule Classification	LIDC-IDRI	Local-Global	AUC	95.62	#6 of 8	Archive leaderboard	report
Lung Nodule Classification	LIDC-IDRI	Local-Global	Accuracy	88.46	#6 of 8	Archive leaderboard	report
Lung Nodule Classification	LIDC-IDRI	Local-Global	Accuracy(10-fold)	88.46	#6 of 8	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling Non-Local Block Non-Local Operation ReLU Residual Block Residual Connection Softmax

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