Fusion of brain imaging genetic data for alzheimer’s disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks DOI
Wei Peng,

Yanhan Ma,

C Li

et al.

Neural Networks, Journal Year: 2024, Volume and Issue: 184, P. 107020 - 107020

Published: Dec. 20, 2024

Language: Английский

Diagnosis of Alzheimer’s disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data DOI
Luyun Wang, Jinhua Sheng, Qiao Zhang

et al.

Cerebral Cortex, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 4, 2025

Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of beneficial for its prevention intervention treatment. In this study, we propose a novel framework, FusionNet-ISBOA-MK-SVM, which integrates fusion network (FusionNet) improved secretary bird optimization algorithm to optimize multikernel support vector machine diagnosis. The model leverages multimodality data, including functional magnetic resonance imaging genetic information (single-nucleotide polymorphisms). Specifically, FusionNet employs U-shaped hierarchical graph convolutional networks sparse attention select feature effectively. Extensive validation using the Disease Neuroimaging Initiative dataset demonstrates model's superior interpretability classification performance. Compared other state-of-the-art learning methods, FusionNet-ISBOA-MK-SVM achieves accuracies 98.6%, 95.7%, 93.0%, 91.8%, 93.1%, 95.4% HC vs. AD, EMCI LMCI EMCI, LMCI, respectively. Moreover, proposed identifies affected brain regions pathogenic genes, offering deeper insights into mechanisms progression disease. These findings provide valuable scientific evidence preventive strategies

Language: Английский

Citations

0
The fuzzy hypergraph neural network model based on sparse k-nearest neighborhood granule DOI
Tao Yin, Weiping Ding, Hengrong Ju

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112721 - 112721

Published: Jan. 1, 2025

Language: Английский

Citations

0
ComNC: A unified framework for trends prediction integrating node and concept effects DOI
S. Y. Xiao, Qing Li, Xiaoyue Gong

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129721 - 129721

Published: Feb. 1, 2025

Language: Английский

Citations

0
Inspired by pathogenic mechanisms: A novel gradual multi-modal fusion framework for mild cognitive impairment diagnosis DOI
Xu Tian,

Hong‐Dong Li,

Hanhe Lin

et al.

Neural Networks, Journal Year: 2025, Volume and Issue: 187, P. 107343 - 107343

Published: March 10, 2025

Language: Английский

Citations

0
EAFP-Med: An efficient adaptive feature processing module based on prompts for medical image detection DOI Creative Commons
Xiang Li, Long Lan, Husam Lahza

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 247, P. 123334 - 123334

Published: Jan. 28, 2024

The rapid proliferation of medical imaging technologies presents a significant challenge for cross-domain adaptive image detection, as lesion representations can vary dramatically across technologies. To address this issue, we draw inspiration from large language models to propose EAFP-Med, an efficient feature processing module based on prompts detection. EAFP-Med incorporates prompt-driven dynamic parameter update mechanism, empowering it extract multi-scale features images diverse modalities adaptively. This exceptional flexibility liberates the constraints any particular technique, fostering great adaptability. Furthermore, also serve preprocessing connected model front-end enhance in input images. Moreover, novel disease detection named ST, which utilizes Swin Transformer V2 – Tiny (SwinV2-T) its backbone and connects EAFP-Med. We have compared our method nine state-of-the-art methods. Experimental results show that overall accuracy EAFP Med ST chest X-ray, brain magnetic resonance imaging, skin datasets is 98.47%, 97.60%, 99.06%, respectively, superior all

Language: Английский

Citations

2
Explainable Multi-Modal Learning in Remote Sensing: Challenges and Future Directions DOI
Alexander Günther, Hiba Najjar, Andreas Dengel

et al.

IEEE Geoscience and Remote Sensing Letters, Journal Year: 2024, Volume and Issue: 21, P. 1 - 5

Published: Jan. 1, 2024

Earth observation applications effectively leverage deep learning models to harness the abundantly available remote sensing data. In order use all different modalities relevant a specific task, fusion of these data sources can be achieved using multi-modal techniques. This is especially helpful when input dataset contains both images and tabular data, or temporal spatial resolutions vary across interest. Nevertheless, techniques typically increase in complexity as disparities nature fused increase. The resulting complex suffer from lack explainability transparency, which crucial many sensitive human-related applications. this letter, we describe how research community addresses issue model context learning. We additionally review practices used other application fields identify some most promising methods tailored for networks exploited

Language: Английский

Citations

2
Output feedback pinning control for complex dynamical networks subjected to multiple attacks DOI
Jinyuan Zhang, Yuechao Ma

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 181, P. 114625 - 114625

Published: Feb. 20, 2024

Language: Английский

Citations

1
Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review DOI Creative Commons
Mirko Jerber Rodríguez Mallma, Luis Zuloaga-Rotta, Rubén Borja-Rosales

et al.

Neurology International, Journal Year: 2024, Volume and Issue: 16(6), P. 1285 - 1307

Published: Oct. 29, 2024

In recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area being one its most impactful fields application. However, to be applied reliably, these models must provide users clear, simple, and transparent explanations about medical decision-making process. This systematic review aims investigate use application explainability ML used brain disease studies. A search was conducted three major bibliographic databases, Web Science, Scopus, PubMed, from January 2014 December 2023. total 133 relevant studies were identified analyzed out a 682 found initial search, which context studied, identifying 11 12 techniques study 20 diseases.

Language: Английский

Citations

1
Modal-invariant progressive representation for multimodal image registration DOI
Jiangang Ding,

Yuanlin Zhao,

Lili Pei

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102903 - 102903

Published: Dec. 1, 2024

Citations

1
Machine learning applications in Alzheimer’s disease research: a comprehensive analysis of data sources, methodologies, and insights DOI

Zahra Rezaie,

Yaser M. Banadaki

International Journal of Data Science and Analytics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 10, 2024

Language: Английский

Citations

0