ArticleBioengineering (Basel, Switzerland)2023
AHANet: Adaptive Hybrid Attention Network for Alzheimer's Disease Classification Using Brain Magnetic Resonance Imaging.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 60 citations in OpenAlex.
- A Dual Stream Deep Learning Framework for Alzheimer's Disease Detection Using MRI Sonification.Journal of imaging · 2026Article
- Multimodal Self-Supervised Learning for Early Alzheimer's: Cross-Modal MRI-PET, Longitudinal Signals, and Site Invariance.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer's disease.Physical and engineering sciences in medicine · 2025Article
- Evaluating the Impact of 2D MRI Slice Orientation and Location on Alzheimer's Disease Diagnosis Using a Lightweight Convolutional Neural Network.Journal of imaging · 2025Article
- A Meta-Learning-Based Ensemble Model for Explainable Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.BMC medical imaging · 2025Article
- Stacked CNN-based multichannel attention networks for Alzheimer disease detection.Scientific reports · 2025Article
- Biologically inspired hybrid model for Alzheimer's disease classification using structural MRI in the ADNI dataset.Frontiers in artificial intelligence · 2025Article
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- Special Issue: Artificial Intelligence in Advanced Medical Imaging.Bioengineering (Basel, Switzerland) · 2024Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- Fractional gradient optimized explainable convolutional neural network for Alzheimer's disease diagnosis.Heliyon · 2024Article
- Convolutional Neural Network Incorporating Multiple Attention Mechanisms for MRI Classification of Lumbar Spinal Stenosis.Bioengineering (Basel, Switzerland) · 2024Article
- Deep CNN ResNet-18 based model with attention and transfer learning for Alzheimer's disease detection.Frontiers in neuroinformatics · 2024Article
- DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images.Frontiers in oncology · 2024Article
- Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.Frontiers in psychiatry · 2024Article
- Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis.EClinicalMedicine · 2023Article
- Self-Supervised Contrastive Learning to Predict the Progression of Alzheimer's Disease with 3D Amyloid-PET.Bioengineering (Basel, Switzerland) · 2023Article
- Alzheimer's disease classification by supervised and intelligent techniques.Journal of Alzheimer's disease reportsArticle
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is a progressive neurological problem that causes brain atrophy and affects the memory and thinking skills of an individual. Accurate detection of AD has been a challenging research topic for a long time in the area of medical image processing. Detecting AD at its earliest stage is crucial for the successful treatment of the disease. The proposed Adaptive Hybrid Attention Network (AHANet) has two attention modules, namely Enhanced Non-Local Attention (ENLA) and Coordinate Attention. These modules extract global-level features and local-level features separately from the brain Magnetic Resonance Imaging (MRI), thereby boosting the feature extraction power of the network. The ENLA module extracts spatial and contextual information on a global scale while also capturing important long-range dependencies. The Coordinate Attention module captures local features from the input images. It embeds positional information into the channel attention mechanism for enhanced feature extraction. Moreover, an Adaptive Feature Aggregation (AFA) module is proposed to fuse features from the global and local levels in an effective way. As a result of incorporating the above architectural enhancements into the DenseNet architecture, the proposed network exhibited better performance compared to the existing works. The proposed network was trained and tested on the ADNI dataset, yielding a classification accuracy of 98.53%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.