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Accurate classification of Alzheimer’s Disease (AD) from brain MRI scans remains a critical yet challenging task due to limited annotated datasets and variability in image quality. This study presents a deep learning pipeline that enhances model robustness by first combining filtered images, denoised using the Non-Local Means (NLM) filter, with their original unfiltered counterparts, creating a hybrid dataset that captures a wide range of image conditions. To further improve training diversity, two augmentation techniques were applied: (1) Generative Adversarial Networks (GANs) for high-quality image generation and resizing, and (2) real-time conventional transformations, including rotation and flipping. The proposed framework was evaluated using three state-of-the-art convolutional neural network architectures—ResNet50V2, DenseNet169, and EfficientNetV2-B0. Among them, EfficientNetV2-B0 achieved the highest classification accuracy of 99%, outperforming the other models and demonstrating strong potential for reliable AD classification.

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Robust Multi-class Classification of Alzheimer’s Disease Using GAN and Conventional Augmentation on Filter-Enhanced Brain MRI Images

  • 会议论文

  • 发表日期：

    2026年

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  • Ali Cherry,
  • Lara Shahrour,
  • Hawraa Hotiet,
  • Mohamad Abou Ali,
  • Mohammad Hajj-Hassan

摘要

Accurate classification of Alzheimer’s Disease (AD) from brain MRI scans remains a critical yet challenging task due to limited annotated datasets and variability in image quality. This study presents a deep learning pipeline that enhances model robustness by first combining filtered images, denoised using the Non-Local Means (NLM) filter, with their original unfiltered counterparts, creating a hybrid dataset that captures a wide range of image conditions. To further improve training diversity, two augmentation techniques were applied: (1) Generative Adversarial Networks (GANs) for high-quality image generation and resizing, and (2) real-time conventional transformations, including rotation and flipping. The proposed framework was evaluated using three state-of-the-art convolutional neural network architectures—ResNet50V2, DenseNet169, and EfficientNetV2-B0. Among them, EfficientNetV2-B0 achieved the highest classification accuracy of 99%, outperforming the other models and demonstrating strong potential for reliable AD classification.