There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Alin Hou |
| Institution: | Changchun University of Technology |
| Department: | School of Computer Science and Engineering |
| Country: | |
| Proposed Analysis: | We will use the convolutional neural network model to realize the auxiliary diagnosis of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal aging (NC). The key technologies and innovations to be solved: 1. Analyze the characteristics and changes of brain MRI images in different stages of Alzheimer's disease, and design a reasonable and feasible convolution neural network model; 2. Optimize the convolution neural network model, adjust the number of convolution kernels and improve the convolution layer frame structure to improve the recognition effect of MRI images of Alzheimer's disease; 3. To study how to improve CNN's recognition ability of brain MRI images under the condition of small samples, and then improve the accuracy of MRI image classification in different stages of Alzheimer's disease. |
| Additional Investigators | |
| Investigator's Name: | Hongkun Ji |
| Proposed Analysis: | We will use the convolutional neural network model to realize the auxiliary diagnosis of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal aging (NC). The Key technologies and innovations to be solved: 1. Analyze the characteristics and changes of brain MRI images in different stages of Alzheimer's disease, and design a reasonable and feasible convolution neural network model; 2. Optimize the convolution neural network model, adjust the number of convolution kernels and improve the convolution layer frame structure to improve the recognition effect of MRI images of Alzheimer's disease; 3. To study how to improve CNN's recognition ability of brain MRI images under the condition of small samples, and then improve the accuracy of MRI image classification in different stages of Alzheimer's disease. |
| Investigator's Name: | Hongkun Ji |
| Proposed Analysis: | We will use the convolutional neural network model to realize the auxiliary diagnosis of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal aging (NC). The key technologies and innovations to be solved: 1. Analyze the characteristics and changes of brain MRI images in different stages of Alzheimer's disease, and design a reasonable and feasible convolution neural network model; 2. Optimize the convolution neural network model, adjust the number of convolution kernels and improve the convolution layer frame structure to improve the recognition effect of MRI images of Alzheimer's disease; 3. To study how to improve CNN's recognition ability of brain MRI images under the condition of small samples, and then improve the accuracy of MRI image classification in different stages of Alzheimer's disease. |
| Investigator's Name: | Hongkun Ji |
| Proposed Analysis: | We will the use convolutional neural network model to realize the auxiliary diagnosis of Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal aging (NC). The key technologies and innovations to be solved: 1. Analyze the characteristics and changes of brain MRI images in different stages of Alzheimer's disease, and design a reasonable and feasible convolution neural network model; 2. Optimize the convolution neural network model, adjust the number of convolution kernels and improve the convolution layer frame structure to improve the recognition effect of MRI images of Alzheimer's disease; 3. To study how to improve CNN's recognition ability of brain MRI images under the condition of small samples, and then improve the accuracy of MRI image classification in different stages of Alzheimer's disease. |
