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Conference Papers Year : 2016

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

Abstract

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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Dates and versions

hal-01778214 , version 1 (25-04-2018)

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  • HAL Id : hal-01778214 , version 1

Cite

Rima Daoudi, Khalifa Djemal. Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection. 8th International Joint Conference on Computational Intelligence (IJCCI 2016), Nov 2016, Porto, Portugal. pp.209--216. ⟨hal-01778214⟩
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