Lung nodule classification by jointly using visual descriptors and deep features

Yutong Xie, Jianpeng Zhang, Sidong Liu, Weidong Cai, Yong Xia*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

13 Citations (Scopus)

Abstract

Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.

Original languageEnglish
Title of host publicationMedical computer vision and Bayesian and graphical models for biomedical imaging
Subtitle of host publicationMICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers
EditorsHenning Muller, B. Michael Kelm, Tal Arbel, Weidong Cai, M. Jorge Cardoso, Georg Langs, Bjoern Menze, Dimitris Metaxas, Albert Montillo, William M. Wells III, Shaoting Zhang, Albert C. S. Chung, Mark Jenkinson, Annemie Ribbens
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Pages116-125
Number of pages10
ISBN (Electronic)9783319611884
ISBN (Print)9783319611877
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventInternational Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 21 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10081 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period21/10/1621/10/16

Keywords

  • Computed tomography
  • Deep convolutional neural network
  • Lung nodule classification
  • Shape descriptor
  • Texture descriptor

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