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An information-theoretic evaluation framework for CNN-LSTM-based Alzheimer's disease classification from structural

Shiva Sanati^1,2, Elias Rahimi¹, Ghosheh Abed Hodtani³

¹Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

Scientific Reports

|June 9, 2026

Summary

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This summary is machine-generated.

This study introduces a CNN-LSTM framework for early Alzheimer's disease (AD) detection using MRI scans. Information-theoretic evaluation enhances classification accuracy and model transparency for potential clinical use.

Area of Science:

Neuroimaging
Artificial Intelligence
Biomedical Engineering

Background:

Early detection of Alzheimer's disease (AD) is crucial for managing its progressive cognitive decline.
Structural Magnetic Resonance Imaging (MRI) offers valuable insights for AD diagnosis.
Existing classification models require robust evaluation methods beyond traditional accuracy metrics.

Purpose of the Study:

To develop and evaluate a CNN-LSTM framework for three-class AD classification (Normal Control, Mild Cognitive Impairment, AD) using structural MRI.
To introduce and assess a novel post-hoc information-theoretic evaluation strategy for AD classification models.
To quantify model performance using metrics like Renyi mutual information, Renyi divergence, and Henze-Penrose divergence.

Main Methods:

Keywords:

Alzheimer’s disease CNN–LSTM GAN-based augmentation Post-hoc information-theoretic evaluation Renyi divergence Structural MRI

Related Experiment Videos

A Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework was designed for AD classification.
Generative Adversarial Networks (GANs) were employed for data augmentation to address data scarcity.
Models were evaluated using conventional metrics and advanced information-theoretic measures on 827 ADNI subjects.

Main Results:

The CNN-LSTM model achieved 96.7% accuracy in subject-level AD classification, outperforming benchmark architectures.
Information-theoretic measures provided complementary insights into model behavior, including information preservation and output distribution alignment.
GAN-based augmentation improved training diversity without compromising validation and test data integrity.

Conclusions:

The proposed CNN-LSTM framework demonstrates high accuracy for AD classification from structural MRI.
Post-hoc information-theoretic analysis offers a more transparent and comprehensive method for evaluating classification models.
Further external validation on multi-center datasets is necessary before clinical implementation.