An Efficient Coding Network Based Feature Extraction with Support Vector Machine Based Classification Model for CT Lung Images

Authors:
  • AOA. Sheryl Oliver
    A. Sheryl Oliver
  • MAM. Anuradha
    M. Anuradha
  • JJJ. Jean Justus
    J. Jean Justus
  • KBKiranmai Bellam
    Kiranmai Bellam
  • TJT. Jayasankar
    T. Jayasankar
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Abstract
Lung cancer is a serious illness affects people all over the globe. To increase the survival rate of patients affected by lung cancer, in advance recognition of lung cancer with effective treatments is important. This study introduces a new deep learning (DL) based feature extraction and classification technique for CT lung images. A DL model using Coding Network (CN) is presented for the extraction of high-level features and classical features. Initially, the convolution neural network is trained as a coding network and the actual pixels are coded into feature vectors for representing the high-level concepts for classification. Next, an extraction of chosen classical features takes place depending upon background knowledge of lung CT images. In addition, an automatic feature fusion takes place to avoid annoying parameter choice. Besides, support vector machine (SVM) model is employed for classify CT lung images in an effective way. For experimentation, a benchmark dataset is utilized to appraise the outcome of the presented CN-SVM model and is validated under several dimensions.