Stacked CNN-based multichannel attention networks for Alzheimer disease detection

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 17, 2025

Alzheimer's Disease (AD) is a progressive condition of neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations in behaviour, and impaired reasoning abilities. Recently, many researchers have been working to develop an effective AD recognition system using deep learning (DL) based convolutional neural network (CNN) model aiming deploy the automatic medical image diagnosis system. The existing still facing difficulties achieving satisfactory performance terms accuracy efficiency because lack feature ineffectiveness. This study proposes lightweight Stacked Convolutional Neural Network with Channel Attention (SCCAN) for MRI on classification overcome challenges. In procedure, we sequentially integrate 5 CNN modules, which form stack generate hierarchical understanding features through multi-level extraction, effectively reducing noise enhancing weight's efficacy. then fed into channel attention module select practical dimension, facilitating selection influential features. . Consequently, exhibits reduced parameters, making it suitable training smaller datasets. Addressing class imbalance Kaggle dataset, balanced distribution samples among classes emphasized. Extensive experiments proposed ADNI1 Complete 1Yr 1.5T, Kaggle, OASIS-1 datasets showed 99.58%, 99.22%, 99.70% accuracy, respectively. model's high surpassed state-of-the-art (SOTA) models proved its excellence significant advancement images.

Language: Английский

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Conversion-aware forecasting of Alzheimer’s disease via featurewise attention

Pattern Analysis and Applications, Journal Year: 2025, Volume and Issue: 28(2)

Published: March 20, 2025

Language: Английский

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Advancements in Alzheimer's disease classification using deep learning frameworks for multimodal neuroimaging: A comprehensive review

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109796 - 109796

Published: Nov. 1, 2024

Language: Английский

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A Custom Convolutional Neural Network Model-Based Bioimaging Technique for Enhanced Accuracy of Alzheimer’s Disease Detection

Published: April 14, 2025

Language: Английский

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Eye tracking based detection of mild cognitive impairment: A review

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103202 - 103202

Published: April 1, 2025

Language: Английский

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Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions

Frontiers in Neuroinformatics, Journal Year: 2025, Volume and Issue: 19

Published: May 2, 2025

Introduction Alzheimer’s disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy predicting progression. Method This narrative review synthesizes current literature on applications using neuroimaging. The process involved comprehensive search of relevant databases (PubMed, Embase, Google Scholar ClinicalTrials.gov ), selection pertinent studies, critical analysis findings. We employed best-evidence approach, prioritizing high-quality studies identifying consistent patterns across the literature. Results Deep architectures, including convolutional neural networks, recurrent transformer-based models, have shown remarkable potential analyzing neuroimaging data. These models can effectively structural functional modalities, extracting features associated with pathology. Integration multiple modalities has demonstrated improved compared single-modality approaches. also promise predictive modeling, biomarkers forecasting Discussion While approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, limited generalizability diverse populations are significant hurdles. clinical translation these requires careful consideration interpretability, transparency, ethical implications. future AI neurodiagnostics looks promising, personalized treatment strategies.

Language: Английский

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Attention-augmented stockwell transform and convolutional neural network framework for electroencephalogram-based multi-class classification of Frontotemporal Dementia

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103267 - 103267

Published: May 1, 2025

Language: Английский

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Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer’s Disease

2021 IEEE International Conference on Big Data (Big Data), Journal Year: 2024, Volume and Issue: unknown, P. 3898 - 3907

Published: Dec. 15, 2024

Language: Английский

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