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<p>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by structural brain changes, often analysed using longitudinal 3D structural Magnetic resonance imaging (sMRI) data. Existing deep learning-based disease progression models face limitations in capturing long-term structural changes by handling class imbalance, and generating realistic future sMRI scans. However, very few works consider predicting future brain MRI images within four years. To address these challenges, a novel Adaptive Regression and ROI-enhanced Generative Adversarial Network (ARE-GAN) is proposed for AD progression prediction with the generation of real future MRI images. This framework integrates an improved generator conditioned on baseline (BL) MRI, an ROI-based attention mechanism to focus on critical brain regions, and an adaptive regression model for precise disease stage estimation. A dedicated feature fusion module is introduced to integrate extracted features from 3D sMRI data and clinical BL information. A discriminator refines the generated sMRI images by distinguishing them from real future MRIs by ensuring realistic spatial fidelity. Additionally, a Self-attention integrated DenseNet (SA-DenseNet) is used as the classification model to categorise the generated MRIs into AD, stable-MCI (s-MCI), progressive-MCI (p-MCI), and normal cognition (NC). From the experimental analysis, the proposed ARE-GAN model attains accuracy of 99.25% based on the ADNI dataset. The proposed ARE-GAN model framework increases the overall accuracy by 12.78%, 8.76%, 7.88% and 11.51% better than 3D CNN, ResNet50V2, 3D ResNet with a CBAM and EfficientNet, respectively.</p>

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ARE-GAN: Adaptive Regression and ROI-Enhanced Generative Adversarial Network for Alzheimer Disease Progression Prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月6日

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  • U Bharathi,
  • S Chitrakala

摘要

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by structural brain changes, often analysed using longitudinal 3D structural Magnetic resonance imaging (sMRI) data. Existing deep learning-based disease progression models face limitations in capturing long-term structural changes by handling class imbalance, and generating realistic future sMRI scans. However, very few works consider predicting future brain MRI images within four years. To address these challenges, a novel Adaptive Regression and ROI-enhanced Generative Adversarial Network (ARE-GAN) is proposed for AD progression prediction with the generation of real future MRI images. This framework integrates an improved generator conditioned on baseline (BL) MRI, an ROI-based attention mechanism to focus on critical brain regions, and an adaptive regression model for precise disease stage estimation. A dedicated feature fusion module is introduced to integrate extracted features from 3D sMRI data and clinical BL information. A discriminator refines the generated sMRI images by distinguishing them from real future MRIs by ensuring realistic spatial fidelity. Additionally, a Self-attention integrated DenseNet (SA-DenseNet) is used as the classification model to categorise the generated MRIs into AD, stable-MCI (s-MCI), progressive-MCI (p-MCI), and normal cognition (NC). From the experimental analysis, the proposed ARE-GAN model attains accuracy of 99.25% based on the ADNI dataset. The proposed ARE-GAN model framework increases the overall accuracy by 12.78%, 8.76%, 7.88% and 11.51% better than 3D CNN, ResNet50V2, 3D ResNet with a CBAM and EfficientNet, respectively.