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Genetic Algorithms for Feature Selection in Computer-Aided Diagnosis

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Computational Intelligence Processing in Medical Diagnosis

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 96))

Abstract

One of the important practical applications of computer vision techniques is computer-aided diagnosis (CAD) in medical imaging. It has been shown that CAD can improve the accuracy of breast cancer detection and characterization by radiologists on mammograms. In this chapter, we discuss an important step — feature selection — in classifier design for CAD algorithms. Feature selection reduces the dimensionality of an available feature space and is therefore often used to prevent over-parameterization of a classifier. Many feature selection techniques have been proposed in the literature. We will illustrate the usefulness of genetic algorithms (GAs) for feature selection by comparing GA with a commonly used sequential selection method. A brief introduction to the GA is given and several examples using GA feature selection for the characterization of mammographic lesions are discussed. The examples illustrate the design of a fitness function for optimizing classification accuracy in terms of the receiver operating characteristics of the classifier, the dependence of GA performance on its evolution parameters, and the design of a fitness function tailored to a specific classification task.

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Authors
  1. B. Sahiner
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  2. H. P. Chan
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  3. N. Petrick
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Editor information

Editors and Affiliations

  1. Technical University of Munich, Ismaninger Straße 22, 81675, München, Germany

    Manfred Schmitt

  2. Romanian Academy, Calea Victoriei 125, Bucharest, Romania

    Horia-Nicolai Teodorescu

  3. The Queens Elizabeth Hospital, Woodville Road, 5011, Woodville, Adelaide, South Australia, Australia

    Ashlesha Jain

  4. Bellevue Residential Care Centre, 5050, Bellevue Heights, Adelaide, South Australia, Australia

    Ajita Jain

  5. Julia Farr Services, Fullarton Road, 5063, Adelaide, South Australia, Australia

    Sandyha Jain

  6. Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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Sahiner, B., Chan, H.P., Petrick, N. (2002). Genetic Algorithms for Feature Selection in Computer-Aided Diagnosis. In: Schmitt, M., Teodorescu, HN., Jain, A., Jain, A., Jain, S., Jain, L.C. (eds) Computational Intelligence Processing in Medical Diagnosis. Studies in Fuzziness and Soft Computing, vol 96. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1788-1_15

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  • DOI: https://doi.org/10.1007/978-3-7908-1788-1_15

  • Publisher Name: Physica, Heidelberg

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