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Different techniques for Alzheimer’s disease classification using brain images: a study

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A Correction to this article was published on 16 November 2021

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Abstract

Alzheimer’s disease (AD) is a kind of dementia that is mostly experienced by people who are in the age of early 60s. In AD, brain cells that are responsible for forming memories and cognitive decisions, get affected which causes overall gray matter shrinkage in the human brain. Since AD patients are growing exponentially in the world, researchers are trying to develop an accurate mechanism for diagnosing the disease using brain images. In this paper, several research articles on AD classification are analyzed along with detailed observations. We have summarized as well as compared the research articles based on their classification performance. Although all the reviewed articles have the potential to classify AD, still there lies major future challenges. Among all the reviewed papers, it is found that the recent deep neural network-based classification techniques can produce the most promising results with an average performance rate of 93%.

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Authors and Affiliations

  1. Department of Information Technology, North Eastern Hill University, Shillong, Meghalaya, 793022, India

    Ruhul Amin Hazarika, Arnab Kumar Maji & Debdatta Kandar

  2. Machine Intelligence Research Labs (MIR Labs) Scientific Network for Innovation and Research Excellence Auburn, Washington, 98071, USA

    Ajith Abraham

  3. Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, 737136, India

    Samarendra Nath Sur

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Hazarika, R.A., Abraham, A., Sur, S.N. et al. Different techniques for Alzheimer’s disease classification using brain images: a study. Int J Multimed Info Retr 10, 199–218 (2021). https://doi.org/10.1007/s13735-021-00210-9

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