Abstract
Alzheimer’s disease (AD) is the most cause of dementia affecting senior’s age staring from 65 and over. The standard criteria for detecting AD is tedious and time consuming. In this paper, an automatic system for AD diagnosis is proposed. A principle of moth-flame optimization is used as features selection algorithm and support vector machine classifier is adopted to distinguish three kinds of classes including Normal, AD and Cognitive Impairment. The main objective of this paper is to aid physicians in detecting AD and to compare two different anatomical views of the brain and identify the best representative one. The performance of this algorithm is evaluated and compared with grey wolf optimizer and genetic algorithm. A benchmark dataset consists of 20 patients for each class is adopted. The experimental results show the efficiency of the proposed system in terms of Recall, Precision, Accuracy and F-Score.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdullah, B.A.: Textural based SVM for MS lesion segmentation in FLAIR MRIs. Open J. Med. Imaging 1, 26–42 (2011)
Beekly, D., Ramos, E., Lee, W.: The national alzheimer’s coordinating center (nacc) database: the uniform data set. Alzheimer Dis. Assoc. Disord. 21(3), 249–258 (2007)
Bicacro, E., Silveira, M., Marques, J.S.: Alternative feature extraction methods in 3D brain image-based diagnosis of Alzheimer’s disease. In: 19th IEEE International Conference on Image Processing (ICIP), Orlando, FL, pp. 1237–1240 (2012)
Davatzikos, C., Fan, Y., Wu, X., Shen, D., Resnick, S.M.: Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol. Aging 29(4), 514–523 (2006)
Fan, Y., Resnick, S.M., Wu, X., Davatzikos, C.: Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. NeuroImage 41(2), 277–285 (2008)
Galloway, M.: Texture analysis using grey level run length. Comput. Graph. Image Process. 4, 172–179 (1975)
Geyer, L.H., DeWald, C.G.: Feature lists and confusion matrices. Percept. Psychophysics 14(3), 471–482 (1973)
Gheyas, I.A., Smith, L.S.: Feature subset selection in large dimensionality domains. Pattern Recogn. 43(1), 5–13 (2010)
Iftekharuddin, K., Zheng, J., Islam, M., Ogg, R.: Fractal-based brain tumor detection in multimodal MRI. Appl. Math. Comput. 207, 23–41 (2009)
Malviya, A., Joshi, A.: Gabor wavelet system for automatic brain tumor detection. Int. J. Emerg. Technol. Adv. Eng. 4, 826–831 (2014)
Mirjalili, S.M.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015). Elsevier
Nassef, T.M.: New segmentation approach to extract human mandible bones based on actual computed tomography data. Am. J. Biomed. Eng. 2(5), 197–201 (2012)
Schroeter, M.L., Stein, T., Maslowski, N., Neumann, J.: Neural correlates of Alzheimer’s disease and mild cognitive impairment a meta-analysis including 1351 patients. NeuroImage 47(4), 1196–1206 (2009)
Sheethal, M., Kannan, B., Varghese, A., Sobha, T.: Intelligent classification algorithm of human brain MRI with efficient wavelet based feature extraction using local binary pattern. In: International Conference on Control Communication and Computing (ICCC), Thiruvananthapuram, pp. 368–375 (2013)
Silveira, M., Marques, J.: Boosting Alzheimer disease diagnosis using pet images. In: International Conference on Pattern Recognition, Istanbul, pp. 2556–2559 (2010)
Solomon, P., Murphyb, C.: Early diagnosis and treatment of Alzheimer’s disease. Expert Rev. Neurother. 8, 769–780 (2008)
Sun, Y., Bhanu, B., Bhanu, S.: Symmetry-integrated injury detection for brain MRI. In: 16th IEEE International Conference on Image Processing (ICIP), Cairo, pp. 661–664 (2009)
Tharwat, A., Gaber, T., Hassanien, A.E.: Cattle identification based on muzzle images using gabor features and SVM classifier. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds.) AMLTA 2014. CCIS, vol. 488, pp. 236–247. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13461-1_23
Wang, B., Yong, Z., Yupu, Y.: Generalized nearest neighbor rule for pattern classification. In: 7th World Congress on Intelligent Control and Automation, Chongqing, pp. 8465–8470 (2008)
Zulpe, N., Pawar, V.: GLCM textural features for brain tumor classification. Int. J. Comput. Sci. Issues 9(3), 354–359 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sayed, G.I., Hassanien, A.E., Nassef, T.M., Pan, JS. (2017). Alzheimer’s Disease Diagnosis Based on Moth Flame Optimization. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-48490-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48489-1
Online ISBN: 978-3-319-48490-7
eBook Packages: EngineeringEngineering (R0)