Earticle

초록

영어

Computer-aided diagnosis (CADx) is usually used as a second opinion by the radiologists. Improving CADx increases the treatment options and a cure is more likely. The main objective of this research is to introduce a new method for feature extraction in order to build a CADx model to discriminate between cancers, benign, and healthy parenchyma. For feature extraction, we use both human features, which are obtained by Digital Database for Screening Mammography (DDSM), and computational features, and we propose a new feature extraction method called Square Centroid Lines Gray Level Distribution Method (SCLGM). The experimental results are obtained from a data set of 410 images taken from DDSM for different types. Our method select 31 features from 145 extracted features; 18 of the selected features are from our proposed feature extraction method (SCLGM). We used both Receiver Operating Characteristics (ROC) and confusing matrix to measure the performance. In training stage, our proposed method achieved an overall classification accuracy of 96.3%, with 92.9% sensitivity and 94.3% specificity. In testing stage, our proposed method achieved an overall classification accuracy of 89%, with 88.6% sensitivity and 83.3% specificity.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Methodology
  3.1 Feature Extraction
  3.2 Feature Selection
 4. Experiments
 5. Conclusions and Future Work
 References

키워드

저자

• Belal K. Elfarra [ Department of Information Systems, Islamic University of Gaza ]
• Ibrahim S. I. Abuhaiba [ Department of Computer Engineering, Islamic University of Gaza ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605