Artificial neural network-based classification system for lung nodules on computed tomography scans

Emre Dandil, Murat Cakiroglu, Ziya Eksi, Murat Ozkan, Ozlem Kar Kurt, Arzu Canan

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

63 Scopus citations

Abstract

Lung cancer is the most common type of cancer among various cancers with the highest mortality rate. The fact that nodules that form on the lungs are in different shapes such as round or spiral in some cases makes their detection difficult. Early diagnosis facilitates identification of treatment phases and increases success rates in treatment. In this study, a holistic Computer Aided Diagnosis (CAD) system has been developed by using Computed-Tomography (CT) images to ensure early diagnosis of lung cancer and differentiation between benign and malignant tumors. The designed CAD system provides segmentation of nodules on the lobes with neural networks model of Self-Organizing Maps (SOM) and ensures classification between benign and malignant nodules with the help of ANN (Artificial Neural Network). Performance values of 90.63% accuracy, 92.30% sensitivity and 89.47% specificity were acquired in the CAD system which utilized a total of 128 CT images obtained from 47 patients.

Original languageEnglish (US)
Title of host publication6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages382-386
Number of pages5
ISBN (Electronic)9781479959341
DOIs
StatePublished - Jan 12 2014
Externally publishedYes
Event6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014 - Tunis, Tunisia
Duration: Aug 11 2014Aug 14 2014

Publication series

Name6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014

Conference

Conference6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014
Country/TerritoryTunisia
CityTunis
Period8/11/148/14/14

Keywords

  • ANN classification
  • CAD
  • CT images
  • Lung cancer
  • Lung nodule

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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