Research And Application Of Classification Method For Alzheimer’s Disease Based On Deep Learning

Alzheimer disease(AD),also known as senile dementia,is a chronic neurodegenerative disease that is highly prevalent in the elderly and is not easily detectable.The development of Alzheimer’s disease is generally divided into three main stages: subjective cognitive impairment(SCI),mild cognitive impairment(MCI)and AD.SCI is the brain aging stage of normal elderly,but does not reach the pathological level.MCI is a syndrome that occurs in the preclinical stage of AD and is also an early signal of AD onset.Due to the slow onset of Alzheimer’s disease and the inability to be completely cured,in the actual clinical application,early detection and correct differentiation of MCI and AD population,giving them effective intervention treatment,to prevent or delay Alzheimer The occurrence of the disease is of great significance.In the clinical diagnosis of AD,the doctor first determines the patient’s disease status based on the basic information such as the patient’s personal information,medical history and neuropsychological assessment,and then further evaluates the severity of the patient’s AD according to the results of various dementia rating scales,and finally determine the type of AD(MCI or AD)based on the patient’s clinical manifestations,genetic examination data,imaging examination data and other auxiliary examinations.It can be seen that to diagnose patients with AD,doctors need to analyze a large number of medical pathology data based on clinical experience.If artificial intelligence technology can be used to assist diagnosis,the efficiency and accuracy of AD diagnosis will be improved help.In this paper,based on deep learning technology,the clinical classification diagnosis of Alzheimer’s disease is studied.The clinical classification model of Alzheimer’s disease based on MRI image presentation text report and Alzheimer’s disease based on multiple ad clinical examination data are proposed respectively.The disease development prediction model is designed to realize the clinical medical auxiliary diagnosis system of Alzheimer’s disease based on these models.The main research contents of this paper include the following three parts:First of all,this paper proposes a clinical classification diagnosis method for Alzheimer’s disease based on MRI image presentation text report.The method uses the MRI image presentation text report of AD patients in a top three hospital as training data,through a combination of Hierarchical Bi-directional Long Short-Term Memory(hierarchical BI-LSTM)and attention mechanism Attention.A classification model was constructed to achieve a clinical classification diagnosis of Alzheimer’s disease.Secondly,this paper proposes a prediction method for the development of Alzheimer’s disease based on multiple AD clinical examination data.The method uses data from the Alzheimer’s Disease Neuroimaging Initiative(ADNI)to include at least three AD clinical examination data within one year of the patient as input data,including basic information about the patient(including age,gender,and education level).25 kinds of characteristic data such as genetic information,genetic information data,neuropsychological examination data,computed tomography data and molecular biological data,and then construct a model using bidirectional LSTM plus Attention mechanism to predict patients after one year.The development trend of Alzheimer’s disease.Finally,this paper designs and implements a deep medical learning clinical diagnosis system for Alzheimer’s disease.The system is based on the Alzheimer’s disease diagnosis classification model and the disease development prediction model.It realizes the disease classification diagnosis based on the patient MRI image performance text report and the disease development prediction based on the patient’s multiple ad clinical examination data.The auxiliary diagnosis provides decision help.