A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation

Pranjal Sahu, Dantong Yu, Mallesham Dasari, Fei Hou, Hong Qin

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a computer-aided system is a challenging task. Unlike previous models that proposed computationally intensive deep ensemble models or three-dimensional CNN models, we propose a lightweight, multiple view sampling based multi-section CNN architecture. The model obtains a nodule's cross sections from multiple view angles and encodes the nodule's volumetric information into a compact representation by aggregating information from its different cross sections via a view pooling layer. The compact feature is subsequently used for the task of nodule classification. The method does not require the nodule's spatial annotation and works directly on the cross sections generated from volume enclosing the nodule. We evaluated the proposed method on lung image database consortium (LIDC) and image database resource initiative (IDRI) dataset. It achieved the state-of-the-art performance with a mean 93.18% classification accuracy. The architecture could also be used to select the representative cross sections determining the nodule's malignancy that facilitates in the interpretation of results. Because of being lightweight, the model could be ported to mobile devices, which brings the power of artificial intelligence (AI) driven application directly into the practitioner's hand.

Original languageEnglish (US)
Article number8525322
Pages (from-to)960-968
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume23
Issue number3
DOIs
StatePublished - May 2019

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Keywords

  • Lung cancer
  • deep learning
  • nodule classification
  • spherical sampling
  • transfer Learning

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