Lung Nodule Classification using A Novel Two-stage Convolutional Neural Networks Structure'.

Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Published Date:

Abstract

Lung cancer is one of the most fatal cancers in the world. If the lung cancer can be diagnosed at an early stage, the survival rate of patients post treatment increases dramatically. Computed Tomography (CT) diagram is an effective tool to detect lung cancer. In this paper, we proposed a novel two-stage convolution neural network (2S-CNN) to classify the lung CT images. The structure is composed of two CNNs. The first CNN is a basic CNN, whose function is to refine the input CT images to extract the ambiguous CT images. The output of first CNN is fed into another inception CNN, a simplified version of GoogLeNet, to enhance the better recognition on complex CT images. The experimental results show that our 2S-CNN structure has achieved an accuracy of 89.6%.

Authors

  • Yang An
    Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
  • Tianren Hu
  • Jiaqi Wang
  • Juan Lyu
  • Sunetra Banerjee
  • Sai Ho Ling
    Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia. Electronic address: Steve.Ling@uts.edu.au.