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: | Yacine Deradra |
| Institution: | facultes des sciences |
| Department: | computer science |
| Country: | |
| Proposed Analysis: | using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data aims to employ deep belief networks (DBNs) for Alzheimer's disease detection and classification using neuroimaging data. Step 1: Preprocessing of the data. This will include cleaning, normalizing, and transforming the neuroimaging data obtained from ADNI to get it into a suitable format for training and testing the DBNs. Step 2: Building the DBNs. This will involve defining the structure of the DBNs, which includes the number of hidden layers and the number of neurons in each layer, and training the DBNs on the preprocessed data. Step 3: Evaluation of the DBNs. This will involve evaluating the performance of the DBNs on both training and testing data, and tuning the parameters of the DBNs to optimize their performance. Step 4: Application of the DBNs to detect and classify Alzheimer's disease. This will involve applying the DBNs to new neuroimaging data, and using the outputs of the DBNs to detect and classify Alzheimer's disease. Step 5: Validation of the results. This will involve validating the results obtained using the DBNs using various evaluation metrics such as accuracy, precision, recall, and F1-score. The results of this analysis will provide insights into the effectiveness of using DBNs for Alzheimer's disease detection and classification using neuroimaging data, and help advance our understanding of Alzheimer's disease and its impact on the brain. |
| Additional Investigators |
