ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images DOI
Rajasekar Mohan, Rajesh Arunachalam, Neha Verma

et al.

Network Computation in Neural Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 41

Published: Dec. 11, 2024

One of the most familiar types disease is Alzheimer's (AD) and it mainly impacts people over age limit 60. AD causes irreversible brain damage in humans. It difficult to recognize various stages AD, hence advanced deep learning methods are suggested for recognizing its initial stages. In this experiment, an effective model-based detection approach introduced provide treatment patient. Initially, essential MRI collected from benchmark resources. After that, gathered MRIs provided as input feature extraction phase. Also, important features image extracted by Vision Transformer-based Residual DenseNet (ViT-ResDenseNet). Later, retrieved applied stage. phase, detected using Adaptive Deep Bayesian Network (Ada-DBN). Additionally, attributes Ada-DBN optimized with help Enhanced Golf Optimization Algorithm (EGOA). So, implemented model accomplishes relatively higher reliability than existing techniques. The numerical results framework obtained accuracy value 96.35 which greater 91.08, 91.95, 93.95 attained EfficientNet-B2, TF- CNN, ViT-GRU, respectively.

Language: Английский

The impact of air pollution on neurodegenerative diseases: a narrative review of current evidence DOI Creative Commons
Nicholas Aderinto,

Ayobami Ajagbe,

Gbolahan Olatunji

et al.

The Egyptian Journal of Internal Medicine, Journal Year: 2025, Volume and Issue: 37(1)

Published: Jan. 24, 2025

Abstract This narrative review explores the relationship between air pollution and neurodegenerative diseases (NDs). Historically, has been linked primarily to respiratory cardiovascular issues, but recent evidence suggests that it may also impact neurological health. With global increase in diseases, understanding environmental risk factors become crucial. The synthesizes findings from studies, highlighting potential role of pollutants—particularly fine particulate matter (PM2.5), ozone, nitrogen dioxide (NO2), heavy metals—in onset progression NDs. Key mechanisms under investigation include brain inflammation microglial activation, which are believed contribute processes. Animal human studies have shown correlations exposure increased cognitive decline disorders. Research indicates exacerbate neuroinflammation cause white abnormalities, disrupt neural communication function. Additionally, emerging like residential greenness climate action could mitigate some these adverse effects. Despite advancements, significant knowledge gaps remain, particularly regarding long-term chronic specific molecular pathways involved. shows need for further research clarify develop targeted interventions. Addressing pollution’s on requires comprehensive public health strategies, including stricter regulations awareness, alongside continued into preventive therapeutic measures.

Language: Английский

Citations

1
Alzheimer’s Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis DOI Creative Commons
Abolfazl Mollalo, George Grekousis, Hermes Flórez

et al.

American Journal of Alzheimer s Disease & Other Dementias®, Journal Year: 2025, Volume and Issue: 40

Published: April 1, 2025

A growing body of literature has examined the impact neighborhood characteristics on Alzheimer’s disease (AD) dementia, yet spatial variability and relative importance most influential factors remain underexplored. We compiled various widely recognized to examine heterogeneity associations with AD dementia prevalence via geographically weighted random forest (GWRF) approach. The GWRF outperformed conventional models an out-of-bag R 2 74.8% in predicting lowest error (MAE = 0.34, RMSE 0.45). Key findings showed that mobile homes were factor 19.9% U.S. counties, followed by NDVI (17.4%), physical inactivity (12.9%), households no vehicle (11.3%), particulate matter (10.4%), while other primary affecting <10% counties. Findings highlight need for county-specific interventions tailored local risk factors. Policies should prioritize increasing affordable housing stability, expanding green spaces, improving transportation access, promoting activity, reducing air pollution exposure.

Language: Английский

Citations

0
ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images DOI
Rajasekar Mohan, Rajesh Arunachalam, Neha Verma

et al.

Network Computation in Neural Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 41

Published: Dec. 11, 2024

One of the most familiar types disease is Alzheimer's (AD) and it mainly impacts people over age limit 60. AD causes irreversible brain damage in humans. It difficult to recognize various stages AD, hence advanced deep learning methods are suggested for recognizing its initial stages. In this experiment, an effective model-based detection approach introduced provide treatment patient. Initially, essential MRI collected from benchmark resources. After that, gathered MRIs provided as input feature extraction phase. Also, important features image extracted by Vision Transformer-based Residual DenseNet (ViT-ResDenseNet). Later, retrieved applied stage. phase, detected using Adaptive Deep Bayesian Network (Ada-DBN). Additionally, attributes Ada-DBN optimized with help Enhanced Golf Optimization Algorithm (EGOA). So, implemented model accomplishes relatively higher reliability than existing techniques. The numerical results framework obtained accuracy value 96.35 which greater 91.08, 91.95, 93.95 attained EfficientNet-B2, TF- CNN, ViT-GRU, respectively.

Language: Английский

Citations

0