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Supervised Classification Techniques for Identifying Alzheimer’s Disease

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018 (AISI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 845))

Abstract

Alzheimer’s Disease is a serious form of dementia. With no current cure, treatments focus on slowing the progression of the disease and controlling its symptoms. Early diagnosis by studying the biomarkers found in structural MRI is the key. This paper proposes a method which combines texture features extracted from gray level co-occurrence matrix and voxel-based morphometry neuroimaging analysis to classify Alzheimer’s disease patients. Different supervised classification techniques are studied, support vector machine, k-nearest neighbor, and decision tree, to obtain best identification accuracy. The paper explores as well the discriminative power for Alzheimer’s disease of certain anatomical regions of interest. The proposed technique is applied on gray matter tissues, and managed successfully to differentiate between Alzheimer’s disease patients and normal controls with accuracy 92%.

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Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Yasmeen Farouk & Sherine Rady

Authors
  1. Yasmeen Farouk
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  2. Sherine Rady
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Corresponding author

Correspondence to Yasmeen Farouk .

Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  4. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Farouk, Y., Rady, S. (2019). Supervised Classification Techniques for Identifying Alzheimer’s Disease. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_17

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