Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection

Rima Daoudi, Khalifa Djemal


We present in this work an Artificial Immune System (AIS) algorithm for breast cancer classification and diagnosis. The main contribution is to select memory cells according to their belonging to a validity interval based on average similarity of training cells. The behaviour of these created memory cells preserves the diversity of original cancer learning class. All these operations allow to generate a set of memory cells with a global representativeness of the database which enables breast cancer classification and recognition. Promising results have been obtained on both Wisconsin Diagnosis Breast Cancer Database (WDBC) and (DDSM) Digital Database for Screening Mammography.


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Paper Citation

in Harvard Style

Daoudi R. and Djemal K. (2016). Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 209-216. DOI: 10.5220/0006057202090216

in Bibtex Style

author={Rima Daoudi and Khalifa Djemal},
title={Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},

in EndNote Style

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection
SN - 978-989-758-201-1
AU - Daoudi R.
AU - Djemal K.
PY - 2016
SP - 209
EP - 216
DO - 10.5220/0006057202090216