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Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network

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Abstract

Alzheimer’s disease, a progressive and irreversible abnormality of the human brain impairs memory and thinking skills. Gradually, it will damage the ability to carry out simple tasks. Even though the disease cannot be completely cured by medical specialists, the rate of brain damage can be pared if the disease is identified in its budding stage itself. Thus, victims and their relatives will get ample time to prepare themselves. Alzheimer’s disease (AD), cognitively normal (CN), mild cognitive impairment convertible (MCIc), and mild cognitive impairment non-convertible (MCInc) are the different phases of cognition. The state of memory loss in aged people, which will not lead to AD, can be encountered as MCInc. The state-MCIc gradually leads to AD. The work is intended for the early detection of AD. Early detection can be claimed if and only if the state-MCIc is detected. But the clinical visual identification of state-MCIc from MRI scan is difficult. In this work, a novel local feature descriptor is proposed for the detection of state-MCIc. The proposed local feature descriptor combined strengths of fast Hessian detector and local binary pattern texture operator for the identification of key points and descriptions. A simple convolutional neural network is used for classification. The classification accuracy between MCIc and CN is obtained as 88.46% which is a pivotal result for early detection of AD. The classification accuracy between AD and CN is attained at 88.99%. The results indicate that the proposed system can contribute a colossal innovation in the early detection of AD.

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Data Availability

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Authors and Affiliations

  1. Department of Electronics and Communication, Karunya Institute of Technology and Sciences, Coimbatore, India

    Ambily Francis & Immanuel Alex Pandian

  2. Department of Electronics and Communication, Sahrdaya College of Engineering and Technology, Kerala, India

    Ambily Francis

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  1. Ambily Francis
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  2. Immanuel Alex Pandian
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Correspondence to Ambily Francis.

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Francis, A., Pandian, I.A. & The Alzheimer’s Disease Neuroimaging Initiative. Early detection of Alzheimer’s disease using local binary pattern and convolutional neural network. Multimed Tools Appl 80, 29585–29600 (2021). https://doi.org/10.1007/s11042-021-11161-y

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  • DOI: https://doi.org/10.1007/s11042-021-11161-y

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