A Novel Hybrid Feature Extraction Model for Classification on Pulmonary Nodules

Document Type : Research Articles

Authors

1 Research Scholar, Research and Development Centre, Bharathiar University,Coimbatore, India.

2 Principal, Sadakathullah Appa College, Tirunelveli,Tamil Nadu, India.

Abstract

In this paper an improved Computer Aided Design system can offer a second opinion to radiologists on early
diagnosis of pulmonary nodules on CT (Computer Tomography) images. A Deep Convolutional Neural Network
(DCNN) method is used for feature extraction and hybridize as combination of Convolutional Neural Network (CNN),
Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradients (ExHOG) and Local Binary Pattern
(LBP). A combination of shape, texture, scaling, rotation, translation features extracted using HOG, LBP and CNN. The
Homogeneous descriptors used to extract the feature of lung images from Lung Image Database Consortium (LIDC)
are given to classifiers Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree and Random
Forest to classify nodules and non-nodules. Experimental results demonstrate the effectiveness of the proposed method
in terms of accuracy which gives best result than the competing methods.

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References

 
Asian Pacific Journal of Cancer Prevention
Volume 20, Issue 2
February 2019
Pages 457-468

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History

  • Receive Date: 16 April 2018
  • Revise Date: 09 November 2018
  • Accept Date: 10 January 2019

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