Alzheimer’s Disease (AD) is a type of dementia which is now known to be the leading cause of death in the United States. Hence, early detection of AD is crucial for treatment planning and preventive measures before patient develops irreversible brain trauma. Deep learning (DL) is a robust machine learning technique used for classification to extract low-to high-level features. Previous studies have used DL to classify functional MRI data of Alzheimers subjects. However, none have employed DL to classify the ealsticity changes in brain MRE data. As a first step towards early diagnosis of AD we have developed a deep recurrent neural learning scheme to classify structural and elasticity changes in brain MRE.