Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Med Image Anal. 2015 Dec;26(1):195-202. doi: 10.1016/ Epub 2015 Sep 8.


In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

Keywords: Chest CT; Convolutional neural networks; Deep learning; Lung cancer screening; OverFeat; Peri-fissural nodules.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Lung Neoplasms / diagnostic imaging*
  • Neural Networks, Computer*
  • Pattern Recognition, Automated / methods*
  • Radiographic Image Enhancement / methods
  • Radiographic Image Interpretation, Computer-Assisted / methods
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Software
  • Solitary Pulmonary Nodule / diagnostic imaging*
  • Subtraction Technique
  • Tomography, X-Ray Computed / methods*