Innovative multi-modal approaches to Alzheimer’s disease detection: Transformer hybrid model and adaptive MLP-Mixer

Abstract

This paper introduces advanced methodologies to enhance Alzheimer’s disease detection. A novel transformer-based hybrid model is proposed, combining adaptive sparse and Multi-head dilated self attention to leverage the unique strengths of both attention mechanisms. Additionally, an innovative adaptive MLP-Mixer model is presented. Several multi-modal fusion techniques are incorporated based on these models. The adaptive MLP-Mixer achieved an accuracy of 96% in mild-level fusion of MRI and DTI modalities. Furthermore, a late fusion method using the same architecture with MRI and sfMRI modalities achieved 98.56% accuracy. For cross-modal fusion, MRI and PET modalities were combined using the transformer-based hybrid model, resulting in an accuracy of 99.98%. Experiments were conducted on the well-known Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets to assess the effectiveness of the proposed methods. Results demonstrated high performance compared to many recent transformer- and CNN-based approaches.