Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer

作者：Yan Tang, Xing Xiong, Gan Tong, Yuan Yang, Hao Zhang · 发表于：BioMedical Engineering OnLine · 年份：2024 · DOI：10.1186/s12938-024-01204-4 · 被引用次数：64 · 研究领域：Dementia and Cognitive Impairment Research、Brain Tumor Detection and Classification、Machine Learning in Healthcare

PURPOSE: Recent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer's disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging. METHODS: The algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD. RESULTS: The model attained a noteworthy classification accuracy of 98.1% for Alzheimer's disease (AD) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area. CONCLUSIONS: A large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the mode...