Early Diagnosis Of Parkinson’s Disease Based On Neuroimaging From Genetic Mutation Group

Parkinson’s disease(PD)is the second common neurodegenerative disease in the elderly.The complications can be divided into motor symptoms and non-motor symptoms.These symptoms are mainly caused by the death of dopaminergic neurons in the brain.With the aging of the population,the number of patients with Parkinson’s disease increases year by year.Given that the current diagnosis of Parkinson’s disease is based on clinical symptoms,the process of diagnosis and treatment is complex,and pathological changes will be caused at this stage.The reason for PD is still unclear,and it takes a lot of manpower and material resources to diagnose the disease clinically.This situation is enough to cause a certain burden on society or individuals.Instead,relevant researchers have found that genes associated with Parkinson’s disease.For families with Parkinson’s disease,these family members can use genetic test to determine whether the relevant gene has been mutated.Once related gene mutations are identified,the population does not even detect clinical symptoms associated with Parkinson’s disease,and they still have the potential to develop Parkinson’s disease.At present,due to the small amount of gene mutation subdivision,most of the computer-aided diagnosis research of Parkinson’s disease is focused on ordinary patients and non-diseased people.And different features can be extracted from neuroimaging data by using pre-processing such as segmentation.We hope to use deep learning to perform multi-feature learning on neuroimaging data,Effective diagnosis and classification of diseases.Based on this,the framework for early diagnosis of Parkinson’s disease based on tandem neuroimaging of gene mutations is mainly composed of the following two parts:First,this paper uses a Stage-wise Hierarchical Deep Polynomial Ensemble Learning(SHDPEL)framework for feature learning of acquired neuroimaging data,where stage-wise learning digs deeper into the information in a single feature,while also obtaining information about the associations between different features.In addition,multiple levels of feature input allow for effective multiplexing of deep and shallow features to provide a discriminatory basis for subsequent disease classification and complete disease classification.Second,this paper constructs a joint feature learning framework using feature selection as well as deep networks together to use neuroimaging data from mutation clusters for feature learning and to further complete disease classification.The framework uses the Cardinality-Constrained Feature-Sample Selection(CCSFS)method based on base constraints for correlation screening of desired features to obtain information on the features most relevant to the disease and use it as feature input for subsequent neural networks.Next,we use a multi-branch Octave Convolution(Oct Conv)neural network for deeper information mining of the screened features.Specifically,by decomposing the high and low frequency information of the features,we reduce the redundant information in the features,save storage and computational resources,improve the speed of network learning,enhance the feature expression ability,and thus improve the classification performance of diseases.In this paper,we have investigated the early diagnosis of Parkinson’s disease in genetic mutation cohorts through a large number of experiments,mainly using different deep learning networks and feature selection methods to learn and classify neuroimaging data features.Also,all experiments in this paper use a 10-fold cross-validation approach to verify the feasibility and validity of the proposed framework.The experimental results show that the framework approach used in this paper can be effective for deep learning of data from mutation clusters and obtain excellent disease classification performance,which is further demonstrated by comparison with existing methods.