Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture

Huseyin Polat, Homay Danaei Mehr

Research output: Contribution to journalArticlepeer-review

91 Citations (Scopus)
43 Downloads (Pure)

Abstract

Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional Softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.
Original languageEnglish
Article number940
Pages (from-to)1-15
Number of pages15
JournalApplied Sciences (Switzerland)
Volume9
Issue number5
DOIs
Publication statusPublished - 6 Mar 2019
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Computed tomography
  • Convolutional neural network
  • Deep learning
  • Lung cancer diagnosis
  • Medical imaging
  • SVM classifier

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