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This is a Experimental Study study.

October 1, 2025Scientific ReportsOpen Access

Advanced MRI based Alzheimer’s diagnosis through ensemble learning techniques

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Authors

SSSuthir SriramAMET University VNV NivethithaAmrita Vishwa Vidyapeetham TKT. P. Arun KaarthicAmrita Vishwa Vidyapeetham

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Implication

Observational analysis improved accuracy of Alzheimer's diagnosis in patients, suggesting advanced MRI methods with ensemble learning enhance detection capabilities.

Key Points

Combining multiple models achieved a diagnosis accuracy of 94.27%, demonstrating the effectiveness of ensemble learning in Alzheimer's detection.
The CNN trained on MRI data alone reached an accuracy of 90.76%, while individual models like ResNet50 and InceptionResNetv2 had 90.27% and 86.84% respectively.
Deep learning techniques showed significant advantages in identifying Alzheimer's disease stages compared to standard CNN approaches.
Using neural networks to analyze MRI results can streamline the early detection of Alzheimer's disease for better treatment outcomes.

Cite This Study

Sriram et al. (2025) studied this question.

synapsesocial.com/papers/68dd89e6fe798ba2fc498100 https://doi.org/10.1038/s41598-025-04899-0

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Implication

Observational analysis improved accuracy of Alzheimer's diagnosis in patients, suggesting advanced MRI methods with ensemble learning enhance detection capabilities.

Advanced MRI based Alzheimer’s diagnosis through ensemble learning techniques

Discussion

Member takes

Implication

Key Points

Cite This Study

Advanced MRI based Alzheimer’s diagnosis through ensemble learning techniques

Discussion

Member takes

Implication

Key Points

Cite This Study

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