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Authors: Rima Daoudi 1 ; Khalifa Djemal 2 and Abdelkader Benyettou 3

Affiliations: 1 University of Evry Val d’Essonne and University of Sciences and Technologies, France ; 2 University of Evry Val d’Essonne, France ; 3 University of Sciences and Technologies, Algeria

ISBN: 978-989-758-052-9

Keyword(s): DDSM, Breast Cancer, Clonal Selection, Local Sets, Median Filter, Clone, Mutate.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Life ; Biocomputing and Complex Adaptive Systems ; Bio-inspired Hardware and Networks ; Computational Intelligence ; Evolutionary Art and Design ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Memetic Algorithms ; Soft Computing ; Symbolic Systems

Abstract: Breast cancer ranks first in the causes of cancer deaths among women around the world. Early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Mammography is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In this aim, Digital Database for Screening Mammography (DDSM) is an invaluable resource for digital mammography research, the purpose of this resource is to provide a large set of mammograms in a digital format. DDSM has been widely used by researchers to evaluate different computer-aided algorithms such as neural networks or SVM. The Artificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system, they are able of learning, memorize and perform pattern recognition. We propose in this paper several enhancements of CLONALG algorithm, one of the most popular algorithms in the AIS field, which are applied on DDSM for br east cancer classification using adapted descriptors. The obtained classification results are 98.31% for CCS-AIS and 97.74% for MF-AIS against 95.57% for original CLONALG. This proves the effectiveness of the used descriptors in the two improved techniques. (More)

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Paper citation in several formats:
Daoudi, R.; Djemal, K. and Benyettou, A. (2014). Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches.In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 244-250. DOI: 10.5220/0005079602440250

@conference{ecta14,
author={Rima Daoudi. and Khalifa Djemal. and Abdelkader Benyettou.},
title={Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},
year={2014},
pages={244-250},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005079602440250},
isbn={978-989-758-052-9},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches
SN - 978-989-758-052-9
AU - Daoudi, R.
AU - Djemal, K.
AU - Benyettou, A.
PY - 2014
SP - 244
EP - 250
DO - 10.5220/0005079602440250

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