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An efficient multi class Alzheimer detection using hybrid equilibrium optimizer with capsule auto encoder

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Abstract

Alzheimer is an advanced nervous brain disease. In old aged people, Alzheimer is also causing the death. The earlier prediction of Alzheimer’s disease (AD) helps to proper treatment and protects from brain tissue damages. In earlier works, different machine learning techniques are presented and the techniques are lacks in the detection performance. This work presented an innovative methodology for the Alzheimer detection in brain image. Initially, an input image is pre-processed by the skull stripping, and normalized linear smoothing and median joint (NLSMJ) filtering. In the next stage, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) brain regions are segmented from the filtered images using adaptive fuzzy based atom search optimizer which is the high convergence rate optimizer for enhancing the segmentation performance. After the image segmentation, GM is registered with the filtered images using the improved affine transformation. Subsequently, features are extracted utilizing improved Zernike features and hybrid wavelet walsh features. Afterwards, features are selected utilizing adaptive rain optimization. Finally, hybrid equilibrium optimizer with capsule auto encoder (HEOCAE) framework is utilized for the detection of Alzheimer, normal and mild cognitive impairment images. The implementation platform used in this work is MATLAB. The presented technique is tested with the ADNI dataset images. The experimental results of the presented technique provide improved performance than the existing techniques in regards of accuracy (98.21%), sensitivity (97.31%), specificity (98.64%), precision (97.45%), NPV (0.098), F1 measure (97.37%) and AUC score (98.29%).

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Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Authors and Affiliations

  1. Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India

    N. P. Ansingkar & Rita. B. Patil

  2. MGM University, Maharashtra, Aurangabad, India

    P. D. Deshmukh

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  1. N. P. Ansingkar
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  2. Rita. B. Patil
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  3. P. D. Deshmukh
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Correspondence to N. P. Ansingkar.

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Ansingkar, N.P., Patil, R.B. & Deshmukh, P.D. An efficient multi class Alzheimer detection using hybrid equilibrium optimizer with capsule auto encoder. Multimed Tools Appl 81, 6539–6570 (2022). https://doi.org/10.1007/s11042-021-11786-z

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  • DOI: https://doi.org/10.1007/s11042-021-11786-z

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