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Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

  • Image & Signal Processing
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Abstract

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya & Joel En WeiKoh

  2. School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, 47500, Subang Jaya, Malaysia

    U. Rajendra Acharya & Chai Hong Yeong

  3. Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  4. Department of Electronics and Communication Engineering, Sahyadri College of Engineering & Management, Mangaluru, India

    Steven Lawrence Fernandes

  5. Department of Medicine, Columbia University, New York, NY, USA

    Edward J. Ciaccio

  6. Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

    Mohd Kamil Mohd Fabell

  7. Department of Computer Science and Information Systems, Texas A&M University-Commerce, Commerce, TX, USA

    U. John Tanik

  8. Department of Electronics and Instrumentation, St. Joseph’s College of Engineering, Chennai, 600119, India

    V. Rajinikanth

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  1. U. Rajendra Acharya
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Correspondence to V. Rajinikanth.

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All procedures performed in studies involving human participants were in accord with the ethical standards of the institutional and/or national research committee (MREC ID No. 2017112–5771) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Acharya, U.R., Fernandes, S.L., WeiKoh, J.E. et al. Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques. J Med Syst 43, 302 (2019). https://doi.org/10.1007/s10916-019-1428-9

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