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: | Pannagaveni P J |
| Institution: | VVCE |
| Department: | Computer Science |
| Country: | |
| Proposed Analysis: | Detection of Alzheimer’s Disease from MRI scan using Machine Learning The methods such as CIT, MMSE and CDR as well as Imaging techniques such as Magnetic Resonance Imaging (MRI), PET and SPE are used to track abnormal changes in the brain and diagnose AD. In the proposed work, the detection of AD is done from MRI scans. The texture, area and shape features are extracted using Gray-Level Co-Occurrence Matrix (GLCM) and moment invariants from the hippocampus which is selected as the Region of Interest (ROI). AD is then classified into various stages based on the features extracted from the ROI using Artificial Neural Network (ANN) which trained using Error Back Propagation (EBP) algorithm. |
| Additional Investigators | |
| Investigator's Name: | Nisarga V |
| Proposed Analysis: | Detection of Alzheimer’s Disease from MRI scan using Machine Learning The methods such as CIT, MMSE and CDR as well as Imaging techniques such as Magnetic Resonance Imaging (MRI), PET and SPE are used to track abnormal changes in the brain and diagnose AD. In the proposed work, the detection of AD is done from MRI scans. The texture, area and shape features are extracted using Gray-Level Co-Occurrence Matrix (GLCM) and moment invariants from the hippocampus which is selected as the Region of Interest (ROI). AD is then classified into various stages based on the features extracted from the ROI using Artificial Neural Network (ANN) which trained using Error Back Propagation (EBP) algorithm. |
