Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease
This addresses early Alzheimer's diagnosis for medical applications, but appears incremental as it combines existing techniques (ResNet, attention) with a fusion method.
The paper tackles Alzheimer's disease detection from 3D MRI images by proposing a model using a pre-trained ResNet backbone with a post-fusion algorithm and attention mechanisms, achieving improved training efficiency and diagnostic accuracy.
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.