Open Access

ARTICLE

CEOE-Net: Chaotic Evolution Algorithm-Based Optimized Ensemble Framework Enhanced with Dual-Attention for Alzheimer’s Diagnosis

Huihui Yang¹, Saif Ur Rehman Khan^2,*, Omair Bilal², Chao Chen^1,*, Ming Zhao²

1 Center for Medical Genetics, Central South University, Changsha, 410083, China
2 School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, 410083, China

* Corresponding Authors: Saif Ur Rehman Khan. Email: ; Chao Chen. Email:

Computer Modeling in Engineering & Sciences 2025, 145(2), 2401-2434. https://doi.org/10.32604/cmes.2025.072148

Received 20 August 2025; Accepted 22 October 2025; Issue published 26 November 2025

Abstract

Detecting Alzheimer’s disease is essential for patient care, as an accurate diagnosis influences treatment options. Classifying dementia from non-dementia in brain MRIs is challenging due to features such as hippocampal atrophy, while manual diagnosis is susceptible to error. Optimal computer-aided diagnosis (CAD) systems are essential for improving accuracy and reducing misclassification risks. This study proposes an optimized ensemble method (CEOE-Net) that initiates with the selection of pre-trained models, including DenseNet121, ResNet50V2, and ResNet152V2 for unique feature extraction. Each selected model is enhanced with the inclusion of a channel attention (CA) block to improve the feature extraction process. In addition, this study employs the Short Time Fourier transform (STFT) technique with each individual model for hierarchical feature extraction before making final predictions in classifying MRI images of dementia and non-demented individuals, considering them as backbone models for building the ensemble method. STFT highlights subtle differences in brain structure and activity, particularly when combined with CA mechanisms that emphasize relevant features by converting spatial data into the frequency domain. The predictions generated from these models are then processed by the Chaotic Evolution Optimization (CEO) algorithm, which determines the optimal weightage set for each backbone model to maximize their contribution. The CEO optimizer explores weight distribution to ensure the most effective combination of model predictions for enhancing classification accuracy, thus significantly improving overall ensemble performance. This study utilized three datasets for validation: two private clinical brain MRI datasets (OSASIS and ADNI) to test the proposed model’s effectiveness. Image augmentation techniques were also employed to enhance dataset diversity and improve classification performance. The proposed CEOE-Net outperforms conventional baseline models and existing methods by showing its effectiveness as a clinical tool for the accurate classification of dementia and non-dementia MRI brain images, as well as autistic and non-autistic facial features. It achieved consistent accuracies of 93.44% on OSASIS and 81.94% on ADNI.

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