Classification of Kidney Lesions Using Bee Swarm Optimization

  • Authors

    • Hima Bindu G
    • Prasad Reddy Pvgd
    • M Ramakrishna Murty
    • S Pallam Setty
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.17905
  • Bee Swarm Optimization, regularization parameter, Support Vector Machine, kernel function.

  • Abstract

    Support Vector Machine (SVM) is extensively used in classification due to its prominent features and better generalisation performance. The classification accuracy is highly dependent on the SVM parameters which are currently selected manually. Therefore, the necessity of an automated, fast and reliable approach to determine optimal SVM parameter and produce high classification accuracy has become important requirement for computer aided detection and diagnostic systems. In the current work, SVM parameters are tuned using Bee Swarm Optimization (BSO) approach to find the probability of achieving better classification accuracy. The approach is studied with two kernel function of SVM – polynomial and Gaussian radial basis function. The algorithm is implemented and executed on kidney CT images for classification of kidney lesions. The BSO-SVM Classification results are compared with SVM classification results obtained on the same dataset and it is found that BSO-SVM classification using BSO optimised SVM parameters produced higher classification accuracy.

     

     

  • References

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  • How to Cite

    Bindu G, H., Reddy Pvgd, P., Ramakrishna Murty, M., & Pallam Setty, S. (2018). Classification of Kidney Lesions Using Bee Swarm Optimization. International Journal of Engineering & Technology, 7(2.33), 1046-1052. https://doi.org/10.14419/ijet.v7i2.33.17905

    Received date: 2018-08-19

    Accepted date: 2018-08-19

    Published date: 2018-06-08