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Hybridized classification approach for magnetic resonance brain images using gray wolf optimizer and support vector machine

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Abstract

Automated abnormal brain discovery is an extremely crucial task for clinical diagnosis. Over a decade ago, various techniques had been displayed to improve this technology. This paper presents a hybrid system based on a combination of Gray Wolf Optimizer (GWO) and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel to classify a given Magnetic Resonance (MR) brain image as benign or malignant. 5-fold cross validation was used to enhance generalization. We applied the hybrid system on 80 images (20 benign and 60 malignant), and found out that the classification accuracy was as high as 98.750%.

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Authors and Affiliations

  1. Informatics Research Institute, City of Scientific Research and Technological Applications, Alexandria, 21934, Egypt

    Heba M. Ahmed & Bayumy A. B. Youssef

  2. Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ahmed S. Elkorany, Adel A. Saleeb & Fathi Abd El-Samie

  3. Engineering Department, Nuclear Research Centre, Atomic Energy Authority, Cairo, Egypt

    Zeinab F. Elsharkawy

Authors
  1. Heba M. Ahmed
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  2. Bayumy A. B. Youssef
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  3. Ahmed S. Elkorany
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  4. Zeinab F. Elsharkawy
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  5. Adel A. Saleeb
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  6. Fathi Abd El-Samie
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Correspondence to Ahmed S. Elkorany.

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Ahmed, H.M., Youssef, B.A.B., Elkorany, A.S. et al. Hybridized classification approach for magnetic resonance brain images using gray wolf optimizer and support vector machine. Multimed Tools Appl 78, 27983–28002 (2019). https://doi.org/10.1007/s11042-019-07876-8

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  • DOI: https://doi.org/10.1007/s11042-019-07876-8

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