Alzheimer’s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer’s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50.

阿尔茨海默病（AD）是一种进行性神经退行性疾病，起病隐匿且不可逆转。认识AD的疾病阶段并进行有效的介入治疗对于减缓和控制疾病的进展非常重要。但由于采集的数据量分布不均衡、AD不同疾病阶段特征变化不明显、特征区域（海马区、内侧颞叶等）分散、狭窄等问题，对 AD 的有效识别仍然是一个未满足的关键需求。因此，我们首先使用数据扩展和合成少数过采样技术（SMOTE）进行类平衡操作，以避免 AD MRI 数据集在训练中受到分类不平衡的影响。随后，提出了一种以ResNet50为骨干网络、基于多模体卷积（MPC）和空间转换注意机制（MPC-STANet）的识别网络，用于AD疾病阶段的识别。在本研究中，我们以根据通道方向进行卷积的方式提出了Multi-Phantom Convolution，并将其与平均池化层集成到ResNet50的两个基本块：Conv Block和Identity Block中，从而提出了Multi-Phantom Residual Block（ MPRB）包括Multi-Conv Block和Multi-Identity Block，以更好地识别阿尔茨海默病分散且微小的疾病特征。同时，使用空间转换注意机制（SCAM）从垂直和水平方向提取权重系数，以更好地识别AD MRI图像中的细微结构变化。实验结果表明，我们提出的方法平均识别准确率为96.25%，F1分数为95%，mAP为93%，参数数量仅比ResNet50多1.69 M。