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AlzONet: a deep learning optimized framework for multiclass Alzheimer’s disease diagnosis using MRI brain imaging

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Abstract

Alzheimer’s disease (AD), characterized by progressive neurological degeneration and cognitive decline, necessitates early detection for effective intervention before symptom onset. Deep learning (DL) methodologies have emerged as promising tools for predicting and classifying AD. In this context, Convolutional Neural Networks (CNNs) exhibit proficiency in discerning specific AD features, enabling accurate diagnosis. To this end, this study proposes an effective deep learning optimized CNN model, namely, AlzONet, tailored to address the intricate challenges of Alzheimer’s patient brain classification. To explore the generalization of the AlzONet model through three gradient optimization algorithms: Adam, SGD, and RMSProp, this study focuses on how each algorithm impacts the model’s ability to minimize the loss function during training and how well it generalizes to new, unseen data. The Kaggle AD dataset, which includes normal, mild, very mild, and moderate stages, assesses the model’s performance. K fold cross-validation is applied to evaluate the model’s efficacy and generalization capability reliably. In contrast, a transfer learning-based comparison was conducted with five pre-trained models (VGG-16, DenseNet-121, ResNet-50, Inception-V3, and Xception). The results reveal that AlzONet trained with Adam achieves exceptional accuracy of 98.1% with a learning rate of 0.0001, while SGD and RMSProp yield 97.3% and 96.6% with a learning rate of 0.001 during training. In the testing phase, the optimized AlzONet model with Adam surpasses expectations with 96.5% accuracy, 96.7% F1-score, and 99.7% AUC.

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Data Availability Statements

The authors declare that they have accessed Data Availability. (n.d.). 2022. Retrieved April 1, 2024, from https://www.kaggle.com/datasets/uraninjo/augmented-alzheimer-mri-dataset

Abbreviations

AD:

Alzheimer’s disease

Adam:

Adaptive moment estimation

AdaGrad:

Adaptive gradient algorithm

AUC:

Area under the curve

AWS:

Amazon web services

CAD:

Computer-aided diagnosis

Caps Net:

Capsule networks

CNN:

Convolutional neural network

DenseNet121:

Densely connected convolutional network 121-layer network

DL:

Deep learning

ET:

Extremely randomized tree

FDA:

Food and drug administration

FN:

False negative

FP:

False positive

FPR:

False-positive rate

Gaussian NB:

Gaussian Naive Bayes

GRNN:

General regression neural network

LR:

Logistic regression

LR-SGD:

Logistic regression with stochastic gradient descent

LSTM:

Long short-term memory

MCI:

Mild cognitive impairment

ML:

Machine learning

MRI:

Magnetic resonance imaging

MPNN:

Multilayer perceptron neural network

NMDA:

N-Methyl-D-Aspartate

RBNN:

Radial basis neural network

PET:

Positron emission tomography

PRC:

Precision-recall curve

ResNet-50:

Residual network 50-layer network

RESTful API:

Representational state transfer application programming interface

RMSProp:

Root mean square propagation

SGD:

Stochastic gradient descent

SMOTE:

Synthetic minority over-sampling technique

SVM:

Support vector machine

TN:

True negative

TP:

True positive

TPR:

True-positive rate

XAL:

Explainable artificial intelligence

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  1. Department of Computer Engineering, University of Basrah, Basrah, Iraq

    Hiba A. Alahmed & Ghaida A. Al-Suhail

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Contributions

Conceptualization, G.A.; Methodology, all authors; Data curation, H.A.; Formal analysis, all authors; Investigation, all authors.; Project administration, G.A.; Python Software Programming, H.A..; Supervision, G.A.; Validation, G.A.; Visualization and Figures, H.A. and G.A.; Writing—original draft, all authors; Writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Ghaida A. Al-Suhail.

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Alahmed, H.A., Al-Suhail, G.A. AlzONet: a deep learning optimized framework for multiclass Alzheimer’s disease diagnosis using MRI brain imaging. J Supercomput 81, 423 (2025). https://doi.org/10.1007/s11227-025-06924-5

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