CNN and swin-transformer based efficient model for Alzheimer’s disease diagnosis with sMRI

Abstract

Alzheimer's disease (AD) is a primary cause of dementia. Its early diagnosis is crucial to delay the progression of the disease. So far, many computer aided diagnosis (CAD) methods that combined deep learning algorithms and structural MRI have achieved encouraging results. To improve the AD diagnosis performance, more and more models are based on 3D algorithms, which make the training and deployment of these methods unaffordable. In this study, a CNN and swin-transformer based efficient model, Efficient Conv-Swin Net (ECSnet), was developed. In this model: (1) a 2.5D-subject method and two-stream structure are used to help the model to encode 3D information to 2D feature maps; (2) convolution blocks are applied in the early stages of the transformer-based backbone network to improve the generalization ability; (3) a series of lightweight approaches are applied to reduce the parameters and computational cost of the model to enable the model to train and infer efficiently. Due to the lack of multi-center data and the differences between test sets, it is difficult to make a fair comparison between the previous methods. Our model was trained on the ADNI dataset and evaluated on an independent test set from AIBL. After being lightened, our proposed method showed no performance degradation on both ADNI and AIBL compared to models such as swin-T tiny. The ECSnet achieved 92.8% balance accuracy and 91.1% sensitivity on the AIBL, which are better than those of previous works while the model is more efficient than those 3D methods.