Krill Herd Optimized Feature Selection for Classification of Alzheimer’s Disease from MRI Images

  • Authors

    • S Sumanth
    • Dr. A Suresh
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.28.28351
  • Magnetic Resonance Imaging (MRI, , Alzheimer’s Disease (AD)/ Dementia, Feature Selection (FS), Krill Herd Optimized Feature Selection, Fuzzy classifier and Neural Network.

  • Abstract

    Alzheimer’s disease (AD) is a type of dementia that is difficult to detect based on clinical surveillances. AD detection on brain Magnetic Resonance Imaging (MRI) data is major anxiety in the neurosciences. Conventional evaluation of efficient image scans in general relies on manual reorientation, visual reading and semi quantitative exploration in brain sections. The Feature Selection (FS) has been tackled to a greater extent since it has proved itself to be a technique that is able to solve the computational problems that are NP-hard and for finding some optimal feature subsets. The FS works by means of removing the features which are irrelevant or redundant. Here in this work, a Krill Herd Optimized Feature Selection has been proposed for the classification of the MRI images. Using the Krill Herd Algorithm (KHA) happens to be widely accepted recently. This is owing to the fact that it represents a modern optimization that is effective and is a good search process. The segregation of the images from brain MRI into either normal or abnormal is important for analysing a normal patient and considering the ones that have higher chances of abnormalities. This technique of classification known as the fuzzy classifier along with the Neural Network has been proposed for getting a better performance that was accurate.

     

     

  • References

    1. [1] Hacibeyoglu, Mehmet, and Mohammed H. Ibrahim (2018). "A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training." Scientific Programming.

      [2] Zhang, Sipeng, Wei Jiang, and Shin'ichi Satoh (2018). "Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm." IEICE Transactions on Information and Systems 101, no. 8: 2064-2071.

      [3] Kowalski, Piotr A., and SzymonÅukasik (2015). "Experimental study of selected parameters of the krill herd algorithm." In Intelligent Systems' 2014, pp. 473-485. Springer, Cham.

      [4] Bhonsle, Devanand, Vivek Kumar Chandra, and G. R. Sinha (2018). "An Optimized Framework Using Adaptive Wavelet Thresholding and Total Variation Technique for De-noising Medical Images." 953-965.

      [5] Zeng, N., Qiu, H., Wang, Z., Liu, W., Zhang, H., & Li, Y. (2018). A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing.

      [6] Casillas, J., Cordón, O., Del Jesus, M. J., & Herrera, F. (2001). Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. Information Sciences, 136(1-4), 135-157.

      [7] Wang, Gai-Ge, Lihong Guo, Amir H. Gandomi, Guo-Sheng Hao, and Heqi Wang (2014). "Chaotic krill herd algorithm." Information Sciences 274: 17-34.

      [8] Rodrigues, D., Pereira, L. A., Papa, J. P., & Weber, S. A. (2014, August). A binary krill herd approach for feature selection. In Pattern Recognition (ICPR), 2014 22nd International Conference on (pp. 1407-1412). IEEE.

      [9] Kowalski, P. A., &Åukasik, S. (2016). Training neural networks with krill herd algorithm. Neural Processing Letters, 44(1), 5-17.

      [10] Joans, S. M., & Sandhiya, J. A Genetic Algorithm Based Feature Selection for Classification of Brain MRI Scan Images Using Random Forest Classifier. International Journal of Advanced Engineering Research and Science, 4(5).

      [11] Kharrat, A., Gasmi, K., Messaoud, M. B., Benamrane, N., &Abid, M. (2010). A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo journal of sciences, 17(1), 71-82.

      [12] Vaishali, R., Sasikala, R., Ramasubbareddy, S., Remya, S., &Nalluri, S. (2017, October). Genetic algorithm based feature selection and MOE Fuzzy classification algorithm on Pima Indians Diabetes dataset. In Computing Networking and Informatics (ICCNI), 2017 International Conference on (pp. 1-5). IEEE.

      [13] [A. H. Gandomi and A. H. Alavi, (2012) “Krill herd: A new bio-inspired optimization algorithm,†Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 12, pp. 4831–4845.

      [14] Abualigah, Laith Mohammad, AhamadTajudinKhader, Mohammed Azmi Al-Betar, and Mohammed A. Awadallah. (2016) "A krill herd algorithm for efficient text documents clustering." In Computer Applications & Industrial Electronics (ISCAIE), 2016 IEEE Symposium on, pp. 67-72. IEEE.

      [15] Cilimkovic, M. (2015). Neural networks and back propagation algorithm. Institute of Technology Blanchardstown, Blanchardstown Road North Dublin, 15.

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  • How to Cite

    Sumanth, S., & A Suresh, D. (2018). Krill Herd Optimized Feature Selection for Classification of Alzheimer’s Disease from MRI Images. International Journal of Engineering & Technology, 7(4.28), 729-734. https://doi.org/10.14419/ijet.v7i4.28.28351

    Received date: 2019-03-14

    Accepted date: 2019-03-14

    Published date: 2018-11-30