Lung nodule classification using deep Local-Global networks.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 24, 2019

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.

Authors

  • Mundher Al-Shabi
    Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia. mundher.al-shabi@monash.edu.
  • Boon Leong Lan
    Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia.
  • Wai Yee Chan
    Department of Biomedical Imaging, University of Malaya, 50603, Kuala Lumpur, Malaysia.
  • Kwan-Hoong Ng
    Department of Biomedical Imaging, University of Malaya, 50603, Kuala Lumpur, Malaysia.
  • Maxine Tan
    School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA. maxine.y.tan-1@ou.edu.

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