Transformer and Convolutional Neural Network: A Hybrid Model for Multimodal Data in Multiclass Classification of Alzheimer’s Disease DOI Creative Commons
Abdulaziz Alorf

Mathematics, Journal Year: 2025, Volume and Issue: 13(10), P. 1548 - 1548

Published: May 8, 2025

Alzheimer’s disease (AD) is a form of dementia that progressively impairs person’s mental abilities. Current classification methods for the six AD stages perform poorly in multiclass and are computationally expensive, which hinders their clinical use. An efficient, low-computational model accurate across all needed can integrate both local global feature extraction. This study uses rs-fMRI, data, transformer-based models to classify stages. The proposed network hybrid two architectures, namely transformer convolutional neural (CNN). addresses by examining brain’s functional connectivity networks based on rs-fMRI data from Disease Neuroimaging Initiative (ADNI). architecture leverages CNNs extraction transformers context; this method employs contextual attention power improve accuracy AD. k-fold cross-validation was employed evaluate performance model. For stages, average 96%. binary classification, accuracies were 98.96% (AD vs. MCI), 99.65% CN), 98.44% LMCI), 96.88% EMCI), 98.36% SMC). These results highlight potential achieving high multistage with limited computational resources. also compared benchmark algorithms outperformed them; it substantially less expensive while maintaining its accuracy.

Language: Английский

Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification DOI Creative Commons
Elnaz Vafaei, Mohammad Hosseini

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1293 - 1293

Published: Feb. 20, 2025

Transformers have rapidly influenced research across various domains. With their superior capability to encode long sequences, they demonstrated exceptional performance, outperforming existing machine learning methods. There has been a rapid increase in the development of transformer-based models for EEG analysis. The high volumes recently published papers highlight need further studies exploring transformer architectures, key components, and employed particularly studies. This paper aims explore four major architectures: Time Series Transformer, Vision Graph Attention hybrid models, along with variants recent We categorize according most frequent applications motor imagery classification, emotion recognition, seizure detection. also highlights challenges applying transformers datasets reviews data augmentation transfer as potential solutions explored years. Finally, we provide summarized comparison reported results. hope this serves roadmap researchers interested employing architectures

Language: Английский

Citations

1
Innovative multi-modal approaches to Alzheimer’s disease detection: Transformer hybrid model and adaptive MLP-Mixer DOI
Rahma Kadri, Bassem Bouaziz,

Mohamed Tmar

et al.

Pattern Recognition Letters, Journal Year: 2025, Volume and Issue: 190, P. 15 - 21

Published: Feb. 7, 2025

Language: Английский

Citations

0
A novel multimodal deep learning framework for predicting residual strength of corroded rectangular hollow-section columns DOI
Yujia Zhang, Yu Zhou, Yu Zhou

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 149, P. 110554 - 110554

Published: March 22, 2025

Language: Английский

Citations

0
AI-driven deep learning framework for enhanced neurodegenerative disease diagnosis: A novel CNN with attention mechanisms and data balancing DOI

Nikhil Pateria,

Dilip Kumar

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 281, P. 127485 - 127485

Published: April 15, 2025

Language: Английский

Citations

0
Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review DOI Creative Commons
Zia‐ur‐Rehman, Mohd Khalid Awang, Ghulam Ali

et al.

Health Science Reports, Journal Year: 2025, Volume and Issue: 8(5)

Published: May 1, 2025

ABSTRACT Purpose Alzheimer's disease (AD) is a severe neurological that significantly impairs brain function. Timely identification of AD essential for appropriate treatment and care. This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging diagnosis, where popular imaging types, reviews well‐known online accessible data sets, describes different algorithms used DL the correct initial evaluation are presented. Significance Conventional diagnostic techniques, including medical evaluations cognitive assessments, usually not identify stages Alzheimer's. Neuroimaging methods, when integrated have demonstrated considerable potential enhancing diagnosis categorization AD. models received significant interest due their capability its early phases automatically, which reduces mortality rate cost Method An extensive literature search was performed leading scientific databases, concentrating on papers published from 2021 2025. Research leveraging techniques such as magnetic resonance (MRI), positron emission tomography, functional (fMRI), so forth. The complies Preferred Reporting Items Systematic Reviews Meta‐Analyses (PRISMA) guidelines. Results Current show CNN‐based especially those utilizing hybrid transfer frameworks, outperform conventional methods. employing combination multimodal has enhanced precision. Still, challenges method interpretability, heterogeneity, limited exist issues. Conclusion considerably improved accuracy reliability neuroimaging. Regardless issues accessibility adaptability, studies into interpretability fusion provide clinical application. Further research should concentrate standardized rigorous validation architectures, understandable AI methodologies enhance effectiveness methods prediction.

Language: Английский

Citations

0
Transformer and Convolutional Neural Network: A Hybrid Model for Multimodal Data in Multiclass Classification of Alzheimer’s Disease DOI Creative Commons
Abdulaziz Alorf

Mathematics, Journal Year: 2025, Volume and Issue: 13(10), P. 1548 - 1548

Published: May 8, 2025

Alzheimer’s disease (AD) is a form of dementia that progressively impairs person’s mental abilities. Current classification methods for the six AD stages perform poorly in multiclass and are computationally expensive, which hinders their clinical use. An efficient, low-computational model accurate across all needed can integrate both local global feature extraction. This study uses rs-fMRI, data, transformer-based models to classify stages. The proposed network hybrid two architectures, namely transformer convolutional neural (CNN). addresses by examining brain’s functional connectivity networks based on rs-fMRI data from Disease Neuroimaging Initiative (ADNI). architecture leverages CNNs extraction transformers context; this method employs contextual attention power improve accuracy AD. k-fold cross-validation was employed evaluate performance model. For stages, average 96%. binary classification, accuracies were 98.96% (AD vs. MCI), 99.65% CN), 98.44% LMCI), 96.88% EMCI), 98.36% SMC). These results highlight potential achieving high multistage with limited computational resources. also compared benchmark algorithms outperformed them; it substantially less expensive while maintaining its accuracy.

Language: Английский

Citations

0