• Isha Suwalka
• Navneet Agrawal

The Alzheimer’s disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third,just behind heart disease and cancer, as a cause of death for older people. So, predicting in its early stage may prevent it from progressing and may be of great importance. The diagnosis of Alzheimer’s disease (AD) requires a range of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient computational approach for accurate prediction of the changes in the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient mapping technique for differentiating affected MRI images from normal .This employs unsupervised learning framework with improved adaptive moving self organizing mapping(AMSOM) method. We first apply median filter for denoising and then feature extraction to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The 24 features are captured with most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a clustering over the set of vectors for classifying and clustering faster. To use the best extracted features, we present a hybrid learning framework which embeds the feature vectors in a subspace. Next, using a small set of subjects, we apply clustering method in the created space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer’s and normal condition (MCI/NC).

Adaptive moving fuzzy self organize mapping, clustering, Feature extraction, Self organisedmapping(SOM) .

"An Improved Alzhemeir’s Disease Detection Using Improved Unsupervised Mapping Technique ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 9, page no.371-377, September-2018, Available :http://www.jetir.org/papers/JETIRB006068.pdf

"An Improved Alzhemeir’s Disease Detection Using Improved Unsupervised Mapping Technique ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 9, page no. pp371-377, September-2018, Available at : http://www.jetir.org/papers/JETIRB006068.pdf