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The early and effective diagnosis of Alzheimer's disease(AD) and mild cognitive impairment(MCI) has received increasing attention in recent years, but most of the existing studies do not pay enough attention to the spatial structure information in structural magnetic resonance images(MRI), and do not go deeper to explore the potential connection between spatial slices and slices, thus making the network models less accurate, poor generalization ability. To explore deeper the connection between spatial context slices, this research is designed to develop a new deep learning method to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability.

Background: The early and effective diagnosis of Alzheimer's disease(AD) and mild cognitive impairment(MCI) has received increasing attention in recent years.There are many machine learning and deep learning methods that are widely used in neural image analysis, among which structural magnetic resonance images play an important role in early diagnosis and intervention for patients with Alzheimer's disease and mild cognitive impairment as a data pattern, and many studies have constructed many network models based on structural magnetic resonance images, but most of the existing studies do not pay enough attention to the spatial structure information in structural magnetic resonance images, and do not go deeper to explore the potential connection between spatial slices and slices,thus making the network models less accurate, poor generalization ability and other problems.We believe that the lesion areas are highly correlated with other areas,and there is a certain relationship between spatial context slices.To explore deeper the connection between spatial context slices and solve the above problems,this research is designed to develop a new deep learning method to effectively detect or predict AD by digging into the deeper spatial contextual structural information. Results: In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification.In addition, this paper designs a new deep learning framework to adapt spatial contextual association networks for early diagnosis of Alzheimer's disease. The experimental results show that the model has good stability, accuracy and generalization ability.Our experimental results showed an accuracy of 92.6% in the AD/CN group, 74.9% in the AD/MCI group, and 76.3% in the MCI/CN group, and several experiments showed that the model has excellent generalization ability. Conclusions: From the comparison of a large number of experimental results, it can be seen that the spatial contextual domain association network can effectively mine the potential information of spatial contextual structural features. The network has better results for the cases that need to go for spatial structure contextual association features.

To address the difficulty in calculating the nonlinear equation of time difference of arrival (TDOA) positioning, as well as the problem of measurement error in the hybrid time difference of arrival/angle of arrival (TDOA/AOA) positioning algorithm, an improved sparrow search algorithm is proposed to optimize positioning, and the optimization mechanism is retained on the basis of improving the performance of the original algorithm. The maximum likelihood estimation method is used to calculate the objective function, and then, the estimated function of the mobile station is used as the fitness function to generate the initial population of sparrows. Then, using particle swarm optimization, optimize the sparrow search algorithm and obtain the population’s optimal solution in order to obtain the optimal position. The simulation results show that, when compared to the existing algorithm, increasing the number of base stations increases the average accuracy of the sparrow search algorithm (SSA) positioning method by 18.54% and 4.5%, respectively, and, when compared to the proposed particle swarm optimization (PSO) positioning method, by 13.79% and 11.6% as the radius increases. The SSA hybrid positioning algorithm performs better in terms of positioning accuracy, convergence speed, and robustness.