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Ensemble deep learning for Alzheimer's disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and

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Summary

This study introduces a deep learning ensemble model for Alzheimer's disease (AD) diagnosis using MRI scans. The model achieves high accuracy, improving early detection and supporting radiologists in diagnosing this neurodegenerative disorder.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions, characterized by amyloid plaques and neurofibrillary tangles.
  • Current diagnostic methods for AD face limitations in accuracy and are prone to misdiagnosis.
  • The rising prevalence of AD necessitates advanced diagnostic tools for timely intervention.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) ensemble model for accurate Alzheimer's disease (AD) identification from MRI scans.
  • To enhance diagnostic precision by integrating features from multiple pre-trained DL models (VGG16, MobileNet, InceptionResNetV2).
  • To provide a tool that aids radiologists in early AD detection and facilitates prompt treatment.

Main Methods:

  • An ensemble deep learning model was created by combining VGG16, MobileNet, and InceptionResNetV2.
  • The model was trained and evaluated on MRI scans to identify Alzheimer's disease markers.
  • Performance metrics including accuracy, specificity, sensitivity, precision, and F1-score were analyzed.

Main Results:

  • The DL ensemble model achieved a high diagnostic accuracy of 97.93%.
  • Exceptional performance was noted with specificity at 98.04%, sensitivity at 95.89%, and precision at 95.94%.
  • The ensemble approach outperformed contemporary classifiers in identifying AD markers.

Conclusions:

  • Deep learning ensemble models offer a powerful and accurate approach for Alzheimer's disease diagnosis using MRI.
  • The developed model demonstrates significant potential to improve early detection rates and streamline the diagnostic workflow for radiologists.
  • This innovative approach represents a promising advancement in the fight against Alzheimer's disease, enabling earlier intervention and treatment.