Acute Stage of Brain Stroke Diagnosis Using Hybrid Genetic Algorithm for Optimization of Feature Selection and Classifier

  • Authors

    • C Amuthadevi
    • K Meena
    • K Arthi
    2018-03-10
    https://doi.org/10.14419/ijet.v7i2.4.11168
  • Brain stroke diagnosis, MLP, Hybrid genetic algorithm,

  • Brain Stroke is the third leading reason of death or major disabilities and needs computer guided assistance to diagnose at an earliest stage of disease. Stroke results in great physical functioning restrictions, which negatively impacts the quality of life for survivors and also care givers. MRI of brain is mainly used for accurate diagnosis even though its cost is high. In this work, a Hybrid Genetic Algorithm (HGA) is proposed for feature selection with Independent Component analysis and parameters optimization of Multi layer Perceptron (MLP). The classification results are compared with simple KNN and MLP Classifiers.

     

     

  • References

    1. [1] AmuthaDevi, C & Rajagopalan, SP 2013, ‘Brain Stroke Classification Based On Multi-Layer Perceptron Using Watershed Segmentation And Gabor Filter’, Journal of Theoretical and Applied Information Technology, vol.56, no. 2, pp. 410-416.

      [2] Amutha Devi, C & Rajagopalan, SP 2015, ‘Independent Component Analysis With Genetic Algorithm Feature Selection for Ischemic Stroke Classification’, Journal of Pure and Applied Microbiology, vol. 9, Special Issue On Recent Research Challenges in Bio-Medical Applications, pp. 215-226

      [3] Wang, G & Guo, L 2013, ‘A novel hybrid bat algorithm with harmony search for global numerical optimization’, Journal of Applied Mathematics, available from <http://www.hindawi.com/journals/jam/2013/696491/cta/>.

      [4] Boz , O, 2002, ‘Feature Subset Selection by Using Sorted Feature Relevance, In ICMLA,pp.147-153.

      [5] John, GH, Kohavi, R & Pfleger, K 1994, ‘Irrelevant features and the subset selection problem’, In the proceedings of the Eleventh International Conference in Machine Learning, pp. 121-129.

      [6] Othman, MF & Basri, MAM 2011, ‘Probabilistic neural network for brain tumor classification’, In IEEE Second International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 136-138.

      [7] Sridhar, D & Murali Krishna, IV 2013, ‘Brain tumor classification using discrete cosine transform and probabilistic neural network’, In IEEE International Conference on Signal Processing, Image Processing & Pattern Recognition (ICSIPR), pp. 92-96.

      [8] Saritha, M, Joseph, KP & Mathew, AT 2013, ‘Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network’, Pattern Recognition Letters, vol. 34, no. 16, pp. 2151-2156.

      [9] Paul, TU & Bandhyopadhyay, SK 2012, ’Segmentation of Brain Tumor from Brain MRI Images Reintroducing K–Means with advanced Dual Localization Method’, International Journal of Engineering Research and Applications (IJERA), vol. 2, no 3, pp. 226-231.

      [10] Amin, SE & Megeed, MA 2012, ‘Brain tumor diagnosis systems based on artificial neural networks and segmentation using MRI’, In IEEE 8th International Conference on Informatics and Systems (INFOS), pp. MM-119.

      [11] Dahab, DA, Ghoniemy, SS & Selim, GM 2012, ‘Automated Brain Tumor Detection and Identification Using Image Processing and Probabilistic Neural Network Techniques’, International journal of image processing and visual communication, vol. 1, no. 2, pp. 1-8

      [12] Kumari, A & Mehra, R 2014, ‘Design of hybrid method PSO and SVM for detection of brain neoplasm’, International Journal of Engineering and Advanced Technology, vol. 3, no. 4, pp. 262-266

      [13] John, P 2012, ‘Brain tumor classification using wavelet and texture based neural network’, International Journal of Science, Engineering Research, vol. 3, no. 10, pp. 1-7.

      [14] Cabral, C, Silveira, M & Figueiredo, P 2011, ’Decoding visual stimuli using classifier ensembles with optimized feature selection’, In IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 300-304.

      [15] Devi, CN, Chandrasekharan, A, Sundararaman, VK & Alex, ZC 2015, ’Neonatal brain MRI segmentation: A review’, Computers in biology and medicine, vol. 64, pp. 163-178.

      [16] Kharat, KD, Kulkarni, PP & Nagori, MB 2012, ‘Brain tumor classification using neural network based methods’, International Journal of Computer Science and Informatics, vol. 1, no .4, pp.85-90.

      [17] Selvanayaki, K 2013, ‘Intelligent brain tumor tissue segmentation from magnetic resonance image (MRI) using meta heuristic algorithms’, Journal of Global Research in Computer Science, vol. 4, no. 2, pp. 13-20.

      [18] Sachdeva, J, Kumar, V, Gupta, I, Khandelwal, N & Ahuja, CK 2011, December, ‘Multiclass brain tumor classification using GA-SVM’, In the proceedings of IEEE conference on Developments in E-systems Engineering (DeSE), pp. 182-187.

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  • How to Cite

    Amuthadevi, C., Meena, K., & Arthi, K. (2018). Acute Stage of Brain Stroke Diagnosis Using Hybrid Genetic Algorithm for Optimization of Feature Selection and Classifier. International Journal of Engineering & Technology, 7(2.4), 70-75. https://doi.org/10.14419/ijet.v7i2.4.11168