Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer

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C.D. Katsis 1,* I. Gkogkou 2 C.A. Papadopoulos 2 Y. Goletsis 3 P.V. Boufounou 4 G. Stylios 5

1. Technological Educational Institute of the Ionian Islands, Dept. of Applications of Information Technology in Administration & Economy, Lefkada Greece

2. University Hospital of Ioannina, Dept. of Radiology, Ioannina, Greece

3. University of Ioannina, Dept. of Economics, Ioannina, Greece

4. Technological Educational Institute of the Ionian Islands, Dept of Business Administration , Lixouri, Kefalonia Greece

5. Technological Educational Institute of the Ionian Islands, Department of Business Administration , Lefkada Greece

* Corresponding author.


Received: 3 May 2012 / Revised: 26 Aug. 2012 / Accepted: 5 Oct. 2012 / Published: 8 Jan. 2013

Index Terms

Artificial Immune Recognition System, Breast Cancer, Correlation Feature Selection, Decision Trees, Multilayer Perceptron Artificial Neural Networks, Support Vector Machines


In this work, a decision support system for early breast cancer detection is presented. In hard to diagnose cases, different examinations (i.e. mammography, ultrasonography and magnetic resonance imaging) provide contradictory findings and patient is guided to biopsy for definite results. The proposed method employs a Correlation Feature Selection procedure and an Artificial Immune Recognition System (AIRS) and is evaluated using real data collected from 53 subjects with contradictory diagnoses. Comparative results with commonly used artificial intelligence classifiers verify the suitability of the AIRS classifier. The application of such an approach can reduce the number of unnecessary biopsies.

Cite This Paper

C.D. Katsis, I. Gkogkou, C.A. Papadopoulos, Y. Goletsis, P.V. Boufounou, G. Stylios, "Using Artificial Immune Recognition Systems in Order to Detect Early Breast Cancer", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.2, pp.34-40, 2013. DOI:10.5815/ijisa.2013.02.04


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