A multi-scale feature selection module based architecture for the diagnosis of Alzheimer's disease on [¹⁸F]FDG PET

Alzheimer's disease (AD) is a prevalent form of dementia worldwide as a cryptic neurodegenerative disease. The symptoms of AD will last for several years, which brings great mental and economic burden to patients and their families. Unfortunately, the complete cure of AD still faces great challenges. Therefore, it is crucial to diagnose the disease in the early stage.

The Visual Geometry Group (VGG) network serves as the backbone for feature extraction, which could reduce the time cost of network training to a certain extent. In order to better extract image information and pay attention to the association information in the images, the group convolutional module and the multi-scale RNN-based feature selection module are proposed. The dataset employed in the study are drawn from [¹⁸F]FDG-PET images within the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Comprehensive experimental results show that the proposed model outperforms several competing approaches in AD-related diagnostic tasks. In addition, the model reduces the number of parameters of the model compared to the backbone model, from 134.27 M to 17.36 M. Furthermore, the ablation reaserch is conducted to confirm the effectiveness of the proposed module.

The paper introduces a lightweight network architecture for the early diagnosis of AD. In contrast to analogous methodologies, the proposed method yields acceptable results.