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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Conference papers
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https://hal.archives-ouvertes.fr/hal-01778214
Contributor : Frédéric Davesne <>
Submitted on : Wednesday, April 25, 2018 - 2:59:49 PM
Last modification on : Monday, October 28, 2019 - 10:50:22 AM

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  • HAL Id : hal-01778214, version 1

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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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