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Lung nodule classification using deep Local-Global networks.

Mundher Al-Shabi1, Boon Leong Lan2, Wai Yee Chan3

  • 1Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia. mundher.al-shabi@monash.edu.

International Journal of Computer Assisted Radiology and Surgery
|April 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning method for lung nodule malignancy prediction. The approach effectively analyzes nodule shape and density, achieving state-of-the-art results on the LIDC-IDRI dataset.

Keywords:
CancerConvolutional neural networkDeep learningLocal–Global featuresLung nodules

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Lung nodules exhibit diverse characteristics, complicating benign/malignant classification.
  • Accurate classification is crucial for effective lung cancer diagnosis and treatment.

Purpose of the Study:

  • To develop a novel deep learning method for predicting lung nodule malignancy.
  • To analyze both nodule shape/size (global features) and density/structure (local features).

Main Methods:

  • Utilized Residual Blocks (3x3 kernel) for local feature extraction.
  • Employed Non-Local Blocks for efficient global feature extraction via matrix multiplications.
  • Trained and validated on the LIDC-IDRI dataset (1018 CT scans).

Main Results:

  • Achieved an Area Under the Curve (AUC) of 95.62%.
  • Demonstrated superior performance compared to baseline methods.
  • Validated using tenfold cross-validation, excluding nodules with <3 radiologist annotations.

Conclusions:

  • The proposed Local-Global network effectively extracts both local and global features for accurate prediction.
  • Outperformed established architectures like Densenet and Resnet with transfer learning.
  • Offers a promising advancement in automated lung nodule malignancy assessment.