Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification
Background/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global...
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MDPI AG
2025-06-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/15/6/612 |
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| author | Ahmad Muhammad Qi Jin Osman Elwasila Yonis Gulzar |
| author_facet | Ahmad Muhammad Qi Jin Osman Elwasila Yonis Gulzar |
| author_sort | Ahmad Muhammad |
| collection | DOAJ |
| description | Background/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global connectivity patterns, limiting their efficacy in Alzheimer’s disease (AD) classification. Methods: This research proposes a novel deep learning framework for multi-stage Alzheimer’s disease (AD) classification using T1-weighted MRI scans. The adaptive feature fusion layer, a pivotal advancement, facilitates the dynamic integration of features extracted from a ResNet50-based CNN and a vision transformer (ViT). Unlike static fusion methods, our adaptive feature fusion layer employs an attention mechanism to dynamically integrate ResNet50’s localized structural features and vision transformer (ViT) global connectivity patterns, significantly enhancing stage-specific Alzheimer’s disease classification accuracy. Results: Evaluated on the Alzheimer’s 5-Class (AD5C) dataset comprising 2380 MRI scans, the framework achieves an accuracy of 99.42% (precision: 99.55%; recall: 99.46%; F1-score: 99.50%), surpassing the prior benchmark of 98.24% by 1.18%. Ablation studies underscore the essential role of adaptive feature fusion in minimizing misclassifications, while external validation on a four-class dataset confirms robust generalizability. Conclusions: This framework enables precise early Alzheimer’s disease (AD) diagnosis by integrating multi-scale neuroimaging features, empowering clinicians to optimize patient care through timely and targeted interventions. |
| format | Article |
| id | doaj-art-360c747e3ea74f5db51a08ed248ef2b9 |
| institution | OA Journals |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-360c747e3ea74f5db51a08ed248ef2b92025-08-20T02:24:18ZengMDPI AGBrain Sciences2076-34252025-06-0115661210.3390/brainsci15060612Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease ClassificationAhmad Muhammad0Qi Jin1Osman Elwasila2Yonis Gulzar3School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi ArabiaBackground/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global connectivity patterns, limiting their efficacy in Alzheimer’s disease (AD) classification. Methods: This research proposes a novel deep learning framework for multi-stage Alzheimer’s disease (AD) classification using T1-weighted MRI scans. The adaptive feature fusion layer, a pivotal advancement, facilitates the dynamic integration of features extracted from a ResNet50-based CNN and a vision transformer (ViT). Unlike static fusion methods, our adaptive feature fusion layer employs an attention mechanism to dynamically integrate ResNet50’s localized structural features and vision transformer (ViT) global connectivity patterns, significantly enhancing stage-specific Alzheimer’s disease classification accuracy. Results: Evaluated on the Alzheimer’s 5-Class (AD5C) dataset comprising 2380 MRI scans, the framework achieves an accuracy of 99.42% (precision: 99.55%; recall: 99.46%; F1-score: 99.50%), surpassing the prior benchmark of 98.24% by 1.18%. Ablation studies underscore the essential role of adaptive feature fusion in minimizing misclassifications, while external validation on a four-class dataset confirms robust generalizability. Conclusions: This framework enables precise early Alzheimer’s disease (AD) diagnosis by integrating multi-scale neuroimaging features, empowering clinicians to optimize patient care through timely and targeted interventions.https://www.mdpi.com/2076-3425/15/6/612Alzheimer’s diseasedeep learningconvolutional neural networksvision transformersadaptive feature fusionMRI classification |
| spellingShingle | Ahmad Muhammad Qi Jin Osman Elwasila Yonis Gulzar Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification Brain Sciences Alzheimer’s disease deep learning convolutional neural networks vision transformers adaptive feature fusion MRI classification |
| title | Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification |
| title_full | Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification |
| title_fullStr | Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification |
| title_full_unstemmed | Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification |
| title_short | Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification |
| title_sort | hybrid deep learning architecture with adaptive feature fusion for multi stage alzheimer s disease classification |
| topic | Alzheimer’s disease deep learning convolutional neural networks vision transformers adaptive feature fusion MRI classification |
| url | https://www.mdpi.com/2076-3425/15/6/612 |
| work_keys_str_mv | AT ahmadmuhammad hybriddeeplearningarchitecturewithadaptivefeaturefusionformultistagealzheimersdiseaseclassification AT qijin hybriddeeplearningarchitecturewithadaptivefeaturefusionformultistagealzheimersdiseaseclassification AT osmanelwasila hybriddeeplearningarchitecturewithadaptivefeaturefusionformultistagealzheimersdiseaseclassification AT yonisgulzar hybriddeeplearningarchitecturewithadaptivefeaturefusionformultistagealzheimersdiseaseclassification |