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Alzheimer's disease staging using enhanced inception-ResNet-V2 and improved XceptionNet models for 3D MRI

V Srilakshmi1, Prasad Devarasetty2, V Lakshmi Chetana3

  • 1SCOPE, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh 522237, India.

Journal of Neuroscience Methods
|April 6, 2026
PubMed
Summary

This study introduces an advanced hybrid deep learning model for Alzheimer's disease (AD) detection and classification using 3D MRI scans. The model achieved over 99% accuracy, offering a powerful tool for early AD diagnosis and monitoring.

Keywords:
Adam optimizerAlzheimer's diseaseDeep learningImproved XceptionNet Enhanced inception-ResNet-V2

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Alzheimer's disease (AD) presents a significant diagnostic challenge due to its progressive nature and cognitive impact.
  • Neuroimage analysis and machine learning offer promising avenues for improving AD diagnosis, progression prediction, and detection.

Purpose of the Study:

  • To develop and evaluate an enhanced hybrid deep learning approach for combined AD classification and segmentation.
  • To improve the accuracy and reliability of AD detection and staging using 3D MRI data.

Main Methods:

  • An enhanced hybrid deep learning model combining Inception-ResNet-V2 for multi-class AD classification and an improved XceptionNet for brain region segmentation.
  • Utilized a parallel convolutional neural network (PCNN) to extract spatial features from 3D MRI scans.
  • Validated the approach on the OASIS and ADNI datasets.

Main Results:

  • The proposed hybrid deep learning model achieved exceptional testing accuracy, reaching 99.5% on the OASIS dataset and 99.7% on the ADNI dataset.
  • Demonstrated superior performance in both classification and segmentation tasks compared to existing state-of-the-art deep learning models.
  • Consistently high training and testing accuracy indicate the model's robustness and reliability.

Conclusions:

  • The integration of advanced deep learning architectures significantly enhances the precision of detecting and assessing AD-related brain changes.
  • The developed models provide practical tools for early Alzheimer's disease diagnosis and natural disease progression monitoring.
  • The study highlights the potential of AI in neuroimaging for advancing Alzheimer's disease research and clinical practice.