Research On Alzheimer’s Disease Prediction Method Based On Cross-modal Prototype Generation

Alzheimer’s disease(AD)is a typical dementia disease in the elderly.The clinical symptoms of this disease are complex,such as memory decline,behavioral abnormalities,and cognitive impairment.Currently,there is no effective treatment plan to prevent or reverse the progression of AD.The early diagnosis and accurate identification of AD pose certain challenges,which often leads to delays in treatment.Medically,the development of Alzheimer’s disease is divided into three stages: AD,mild cognitive impairment(MCI),and normal control(NC).At present,magnetic resonance imaging(MRI)has been widely used for the classification of the above three stages.Therefore,using MRI as an early diagnostic imaging data for AD and classifying AD based on it has significant research significance for accurate diagnosis and early intervention of diseases.This article focuses on structural magnetic resonance imaging(s MRI)as the main modal data,and conducts research on AD classification based on cross modal prototypes.Firstly,for the misclassification in AD classification,an adaptive cross modal prototype was proposed based on multimodal knowledge.A domain adaptive network was designed to effectively address the unsatisfactory performance of classification models caused by different data sources in different datasets.The main research content and work of this article include:1)In the case of a single MRI modality,using traditional methods can lead to a large number of MCI misclassification phenomena in AD classification tasks.This article is based on the idea of metric learning,utilizing other modal data to generate cross modal prototypes,and reducing MRI misclassification rate from the perspective of adjusting classification boundaries.In addition,this article proposes an adaptive mechanism to dynamically adjust the proportion of multimodal data,allowing valuable modal features to play a maximum role,thereby improving the usability of the framework.2)The classification performance of the model trained based on the ADNI dataset is not ideal when used on another dataset with different data distributions.In response to the problem of different sources of AD datasets that make it impossible for models to be shared,this article,based on the idea of domain adversarial networks,intervenes in the prototype generation stage of the model in Chapter 3,adding domain discriminators and label classifiers.The two work together to form a cross domain prototype generation network,allowing the feature expression of the prototype to balance the data source and classification effect.3)To assist doctors in diagnosing difficult images,this article combines the above model,uses Spring Boot combined with MVC as the main framework,My SQL and Redis as data storage components,and develops an AD assisted diagnosis system based on cross-modal prototype generation.