  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.
