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: | Krishnakumar Vaithinathan |
| Institution: | Pondicherry University |
| Department: | Computer SCience and Engineering |
| Country: | |
| Proposed Analysis: | An Intelligent System for the diagnosis of Alzheimer’s disease using Extreme Learning Machines Neurodegeneration is the collective term that represents a range of conditions progressively affecting the neurons of the human brain. The continuous deterioration of the nerve cells or neurons affects the memory functions and cognitive abilities of a person. The alzheimer’s disease (AD) is one of the important types of the neurodegenerative diseases and the researchers around the globe develop various kinds of therapies to slow its progressiveness. But it still requires an early diagnosis, for the effective treatment of AD. Nowadays, many computer aided tools are used for the quick detection of the disease based on neural networks, support vector classifiers, etc. The high human intervention and the time taken for the learning process is an important factor to consider, while choosing the above methods. The extreme learning machine (ELM) requires less human involvement and learns at high speed than the conventional methods and it also provides high generalized performance for many nonlinear activation functions. Here the ELM based diagnosis tool is developed for the classification between AD’s patients and normal individual. First, the shape and feature extraction are done on the MRI images. Later, genetic algorithm is used for the reduction of the feature space. Final step is the training of the ELM to do the detection of AD’s disease. It is proposed to use the ADNI database for this purpose. |
| Additional Investigators |
