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Ensemble-of-classifiers-based approach for early Alzheimer’s Disease detection

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Abstract

Alzheimer’s disease (AD) is a deadly neurological condition. Deep learning approaches (DL) techniques have just been utilized to track the evolution of Alzheimer’s disease. These studies only employed baseline neuro imaging data. Because of the high cost of neuro imaging data, it is constantly restricted or unavailable. As a result, this research developed a novel, four-phase early Alzheimer’s disease detection approach: “(a) pre-processing, (b) feature extraction, (c) feature selection, and (d) classification”. Data cleaning and normalization is used in pre-processing. Consequently, features like “Weighted Geometric Mean Principle Component Analysis (WGM-PCA), Statistical Features, higher-order statistical features, and Weighted modified correlation-based features” are retrieved from the pre-processed data. Employing the Improved Attribute Ranker (IAR), the most relevant characteristics are chosen. Furthermore, the disease classification phase is represented by a deep learning model based on an ensemble of classifiers, containing optimized “Bi-GRU, Multi-Layer Perceptron (MLP), and Quantum Neural Network (QDNN)”, respectively. The ultimate decision is obtained via optimal Bi-GRU, which is trained using MLP and QDNN outcomes. Both the MLP and the QDNN would be trained using the chosen IAR-based features. Interestingly, to improve the network’s detection accuracy, the weight of the QDNN model is adjusted using the recently proposed Enhanced Math Optimizer Accelerated Arithmetic Optimization (EMOAOA) technique. Particularly, the proposed EMOAOA+EC achieved detecting accuracies of 95% at the 60th LR, 95.5% at the 70th LR, 98% at the 80th LR, and 98.7% at the 90th LR. The development of the optimized ensemble classifier is responsible for this improvement.

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No new data were generated or analysed in support of this research.

Abbreviations

MM-SDPN:

multimodal stacked DPN

MCC:

Matthews correlation coefficient

ROI:

Regions of interest

CBIR:

Content based image retrieval

ALO:

Ant Lion Optimization

FDG-PET:

Fluoro deoxy glucose positron emission tomography

PSO:

Particle swarm optimization

FDR:

False discovery rate

AD:

Alzheimer’s disease

AUC:

Receiver operating characteristic curve

VBC:

Voxel-based classifier

BGRU:

Bidirectional gated recurrent units

CapsNets:

3d-capsule networks

NPV:

Negative predictive value

PRO:

Poor Rich Optimization

MRI:

Magnetic resonance images

FNR:

False negative rate

CAD:

Computer-aided diagnosis

CNN:

Convolutional neural networks

CluViaN:

Clustering via network

CMBO:

Cat Mouse Optimization Algorithm

SVM:

Support vector machine

RF:

Random forest

SPM:

Statistical parametric mapping

AOA:

Arithmetic optimization algorithm

EC:

Ensemble classifier

AO:

Aquila optimizer

LR:

Learning Rate

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, Tamilnadu, 600 073, India

    RS Rajasree

  2. Department of Computer Science, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, Tamilnadu, 600 073, India

    S Brintha Rajakumari

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All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Rajasree, R., Brintha Rajakumari, S. Ensemble-of-classifiers-based approach for early Alzheimer’s Disease detection. Multimed Tools Appl 83, 16067–16095 (2024). https://doi.org/10.1007/s11042-023-16023-3

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