Abstract
Early and accurate diagnosis of Alzheimer Disease is critical for effective clinical intervention, particularly in distinguishing it from Mild Cognitive Impairment, a prodromal stage marked by subtle structural changes. In this study, we propose a hybrid deep learning ensemble framework for Alzheimer Disease classification using structural magnetic resonance imaging. Gray and white matter slices are used as inputs to three pretrained convolutional neural networks such as ResNet50, NASNet, and MobileNet, each fine tuned through an end to end process. To further enhance performance, we incorporate a stacked ensemble learning strategy with a meta learner and weighted averaging to optimally combine the base models. Evaluated on the Alzheimer Disease Neuroimaging Initiative dataset, the proposed method achieves state of the art accuracy of 99.21% for Alzheimer Disease vs. Mild Cognitive Impairment and 91.0% for Mild Cognitive Impairment vs. Normal Controls, outperforming conventional transfer learning and baseline ensemble methods. To improve interpretability in image based diagnostics, we integrate Explainable AI techniques by Gradient weighted Class Activation, which generates heatmaps and attribution maps that highlight critical regions in gray and white matter slices, revealing structural biomarkers that influence model decisions. These results highlight the frameworks potential for robust and scalable clinical decision support in neurodegenerative disease diagnostics.
Abstract (translated)
阿尔茨海默病的早期和准确诊断对于有效的临床干预至关重要，特别是在将其与轻度认知障碍（MCI）区分开来时。MCI是一个前驱阶段，以细微的结构变化为标志。在本研究中，我们提出了一种使用结构磁共振成像进行阿尔茨海默病分类的混合深度学习集成框架。灰质和白质切片被用作三个预训练卷积神经网络（如ResNet50、NASNet和MobileNet）的输入，每个模型通过端到端的过程进行了微调以适应特定任务。 为了进一步提高性能，我们采用了一种堆叠式集成学习策略，并结合了一个元学习器以及加权平均法来优化地组合基础模型。在阿尔茨海默病神经成像倡议（ADNI）数据集上评估后，所提出的方法实现了99.21%的最高准确率用于区分阿尔茨海默病与轻度认知障碍，以及91.0%的准确性用于区分轻度认知障碍与正常对照组，超越了传统的迁移学习和基准集成方法。 为了提高基于图像诊断的解释性，我们通过梯度加权类激活（Grad-CAM）技术整合了解释人工智能的方法。这种方法生成热图和归因图，突出显示灰质和白质切片中的关键区域，并揭示影响模型决策的结构生物标志物。 这些结果强调了该框架在神经退行性疾病诊断中提供强大且可扩展临床决策支持方面的潜力。
URL
https://arxiv.org/abs/2510.00048
