Alzheimer's disease (AD) is the most common neurodegeneration, annually diagnosed in millions of patients. The present medicine scenario still finds challenges in the exact diagnosis and classification of AD through neuroimaging data. Traditional CNNs can extract a good amount of low-level information in an image but fail to extract high-level minuscule particles, which is a significant challenge in detecting AD from MRI scans. To overcome this, we propose a novel Granular Feature Integration method to combine information extraction at different scales combined with an efficient information flow, enabling the model to capture both broad and fine-grained features simultaneously. We also propose a Bi-Focal Perspective mechanism to highlight the subtle neurofibrillary tangles and amyloid plaques in the MRI scans, ensuring that critical pathological markers are accurately identified. Our model achieved an F1-Score of 99.31%, precision of 99.24%, and recall of 99.51%. These scores prove that our model is significantly better than the state-of-the-art (SOTA) CNNs in existence.

翻译：阿尔茨海默病（AD）是最常见的神经退行性疾病，每年有数百万患者被确诊。当前医疗领域在通过神经影像数据对AD进行精确诊断与分类方面仍面临挑战。传统CNN能够提取图像中的大量低层信息，但难以提取高层细微特征，这是从MRI扫描中检测AD的一个重大难题。为克服这一局限，我们提出一种新颖的细粒度特征集成方法，通过结合多尺度信息提取与高效信息流，使模型能够同时捕获宏观与细粒度特征。我们还提出一种双焦视角机制，用于增强MRI扫描中细微神经纤维缠结和淀粉样蛋白斑块的显着性，确保关键病理标志物被准确识别。我们的模型取得了99.31%的F1分数、99.24%的精确率与99.51%的召回率。这些指标证明，本模型显著优于现有的最先进CNN方法。