An Immune-Inspired Approach for Breast Cancer Classification

  title={An Immune-Inspired Approach for Breast Cancer Classification},
  author={Rima Daoudi and Khalifa Djemal and Abdelkader Benyettou},
Many pattern recognition and machine learning methods have been used in cancer diagnosis. The Artificial Immune System (AIS) is a novel computational intelligence technique. Designed by the principles of the natural immune system, it is able of learning, memorize and perform pattern recognition. The AIS’s are used in various domains as intrusion detection, robotics, illnesses diagnostic, data mining, etc. This paper presents a new immune inspired idea based on median filtering for cloning, and… 
Improving cells recognition by local database categorization in Artificial Immune System algorithm. Application to breast cancer diagnosis
The results obtained on the Digital Database for Screening Mammography (DDSM) show the effectiveness of the proposed classifier either in classification accuracy or computing costs compared to other AIS algorithms.
Diagnosing breast cancer with an improved artificial immune recognition system
A new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS was introduced, and with an accuracy of 100 %, it was able to classify breast cancer dataset successfully.
Breast Cancer Classification by Artificial Immune Algorithm based Validity Interval Cells Selection
An Artificial Immune System (AIS) algorithm for breast cancer classification and diagnosis is presented, to select memory cells according to their belonging to a validity interval based on average similarity of training cells to generate a set of memory cells with a global representativeness of the database.
Using artificial immune algorithm for fast convergence of multi layer perceptron in breast cancer diagnosis application
A significant reduction of computation time has been obtained with a slight improvement of classification accuracy and the results show that combining Artificial Immune Systems and Neural Networks is effective.
Digital Database for Screening Mammography Classification Using Improved Artificial Immune System Approaches
Several enhancements of CLONALG algorithm, one of the most popular algorithms in the AIS field, are proposed in this paper which are applied on DDSM for breast cancer classification using adapted descriptors and proves the effectiveness of the used descriptors in the two improved techniques.
A hybrid approach based on decision trees and clustering for breast cancer classification
Experimental study on Wisconsin Breast Cancer Database provides a thorough analysis of the induced results and shows that it can enhance the classification results by distinguishing different types of Breast Cancer using a clustering technique.
Recent advances in clonal selection algorithms and applications
  • Wenjian Luo, Xin Lin
  • Computer Science
    2017 IEEE Symposium Series on Computational Intelligence (SSCI)
  • 2017
Various types of applications using clonal selection algorithms are summarized, including global optimization, constrained optimization, combinatorial optimization, multiobjective optimization, dynamic optimization and other applications.
Classification du cancer du sein par des approches basées sur les systèmes immunitaires artificiels
Le cancer du sein arrive dans le monde en premiere position en termes d’incidence et de mortalite parmi les differentes localisations cancereuses chez les femmes. Malgre les avancees significatives


A New Classification Method for Breast Cancer Diagnosis: Feature Selection Artificial Immune Recognition System (FS-AIRS)
In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with a new approach, FS-AIRS (Feature Selection Artificial Immune Recognition System)
An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers
This paper proposes a Support Vector Machines based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease and provides the implementation details along with the corresponding results.
WBCD breast cancer database classification applying artificial metaplasticity neural network
Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis
This study compared the particle swarm optimizer based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors.
An expert system for detection of breast cancer based on association rules and neural network
A genetic algorithm based nearest neighbor classification to breast cancer diagnosis
  • R. Jain, J. Mazumdar
  • Computer Science
    Australasian Physics & Engineering Sciences in Medicine
  • 2009
The genetic algorithm based k-nearest neighbour method for classification of benign and malignant breast tumors is presented and results are compared with a fuzzy-genetic approach where each reference pattern represents a fuzzy if-then rule with a circular-cone-type membership function.
Generating Compact Classifier Systems Using a Simple Artificial Immune System
The design of a new AIS algorithm and classifier system called simple AIS, which takes only one B-cell, instead of a B-cells pool, to represent the classifier, and is found to be very competitive when compared to other classifiers.
Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms
Experimental results show that application of the proposed method can significantly reduce the case-base size while the classification performance of the KB-CAD, in fact, increases.
Clonal Selection Algorithm for Classification
An approach for classification using CLONALG with competitive results in terms of classification accuracy, compared to other AIS models and evolutionary algorithms tested on the same benchmark data sets is proposed.
Artificial immune systems as a novel soft computing paradigm
This paper proposes one such framework for AIS, discusses the suitability of AIS as a novel soft computing paradigm and reviews those works from the literature that integrate AIS with other approaches, focusing ANN, EA and FS.