Early diagnosis of Alzheimer Diagnostics (AD) is a challenging task due to its subtle and complex clinical symptoms. Deep learning-assisted medical diagnosis using image recognition techniques has become an important research topic in this field. The features have to accurately capture main variations of anatomical brain structures. However, time-consuming is expensive for feature extraction by deep learning training. This study proposes a novel Alzheimer's disease detection model based on Convolutional Neural Networks. The model utilizes a pre-trained ResNet network as the backbone, incorporating post-fusion algorithm for 3D medical images and attention mechanisms. The experimental results indicate that the employed 2D fusion algorithm effectively improves the model's training expense. And the introduced attention mechanism accurately weights important regions in images, further enhancing the model's diagnostic accuracy.

翻译：阿尔茨海默病(AD)的早期诊断因其临床症状的细微性和复杂性而极具挑战性。基于图像识别技术的深度学习辅助医学诊断已成为该领域的重要研究方向。特征需精确捕捉大脑解剖结构的主要变化，但深度学习训练中的特征提取耗时较高。本研究提出了一种基于卷积神经网络的新型阿尔茨海默病检测模型。该模型以预训练ResNet网络为主干架构，融合了针对三维医学图像的后融合算法与注意力机制。实验结果表明，所采用的二维融合算法有效提升了模型的训练效率；引入的注意力机制能精准加权图像中的重要区域，进一步提高了模型的诊断准确性。