Normalized Multiple Features Fusion Based on PCA and Multiple Classifiers Voting in CT Liver Tumor Recognition

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 730)

Abstract

Liver cancer is a serious disease and is the third commonest cancer followed by stomach and lung cancer. The most effective way to reduce deaths due to liver cancer is to detect and diagnosis in the early stages. In this paper, a fast and accurate automatic Computer-Aided Diagnosis (CAD) system to diagnose liver tumors is proposed. First, texture features are extracted from liver tumors using multiple texture analysis methods and fused feature is applied to overcome the limitation of feature extraction in single scale and to increase the efficiency and stability of liver tumor diagnosis. Classification-based texture features is applied to discriminate between benign and malignant liver tumors using multiple classifier voting. We review different methods for liver tumors characterization. An attempt was made to combine the individual scores from different techniques in order to compensate their individual weaknesses and to preserve their strength. The experimental results show that the overall accuracy obtained is 100% of automatic agreement classification. The proposed system is robust and can help doctor for further treatment.

Keywords

Classification Fusion Feature extraction SVM Feature reduction CAD 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computers and InformationBeni-Suef UniversityBeni SuefEgypt
  2. 2.Faculty of Computers and Information, Information Technology DepartmentCairo UniversityGizaEgypt
  3. 3.Scientific Research Group in Egypt (SRGE)CairoEgypt

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