Alzheimer's disease (AD), the predominant form of dementia, poses a growing global challenge and underscores the urgency of accurate and early diagnosis. The clinical technique radiologists adopt for distinguishing between mild cognitive impairment (MCI) and AD using Machine Resonance Imaging (MRI) encounter hurdles because they are not consistent and reliable. Machine learning has been shown to offer promise for early AD diagnosis. However, existing models focused on focal fine-grain features without considerations to focal structural features that give off information on neurodegeneration of the brain cerebral cortex. Therefore, this paper proposes a machine learning (ML) framework that integrates Gamma correction, an image enhancement technique, and includes a structure-focused neurodegeneration convolutional neural network (CNN) architecture called SNeurodCNN for discriminating between AD and MCI. The ML framework leverages the mid-sagittal and para-sagittal brain image viewpoints of the structure-focused Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through experiments, our proposed machine learning framework shows exceptional performance. The parasagittal viewpoint set achieves 97.8% accuracy, with 97.0% specificity and 98.5% sensitivity. The midsagittal viewpoint is shown to present deeper insights into the structural brain changes given the increase in accuracy, specificity, and sensitivity, which are 98.1% 97.2%, and 99.0%, respectively. Using GradCAM technique, we show that our proposed model is capable of capturing the structural dynamics of MCI and AD which exist about the frontal lobe, occipital lobe, cerebellum, and parietal lobe. Therefore, our model itself as a potential brain structural change Digi-Biomarker for early diagnosis of AD.

翻译：阿尔茨海默病（AD）作为痴呆症的主要形式，正日益构成全球性挑战，凸显了准确早期诊断的紧迫性。放射科医生在临床中采用磁共振成像（MRI）区分轻度认知障碍（MCI）与AD的技术，因缺乏一致性和可靠性而面临困难。机器学习已被证明在早期AD诊断方面具有潜力。然而，现有模型主要关注局部细粒度特征，而未考虑能够反映大脑皮层神经退行性变化的局部结构特征。因此，本文提出一个集成伽马校正（一种图像增强技术）的机器学习框架，并设计名为SNeurodCNN的面向结构的神经退行性卷积神经网络架构，用于区分AD与MCI。该框架利用结构导向的阿尔茨海默病神经影像学倡议（ADNI）数据集中的正中矢状面与旁矢状面脑图像视角。实验表明，我们提出的机器学习框架展现出卓越性能。旁矢状面视角集达到97.8%的准确率，其中特异性为97.0%，敏感性为98.5%。正中矢状面视角在准确率、特异性和敏感性上分别提升至98.1%、97.2%和99.0%，揭示了大脑结构变化的更深层信息。通过GradCAM技术，我们证明该模型能够捕捉MCI与AD在额叶、枕叶、小脑及顶叶区域存在的结构动态变化。因此，本模型自身可作为早期诊断AD的潜在脑结构变化数字生物标志物。