Early detection of Alzheimer disease using Data Augmentation and CNN

Alzheimer’s disease is the most common type of dementia. It is a progressive disease beginning with mild memory loss and possibly leading to loss of the ability to carry on a conversation and respond to the environment. Alzheimer’s disease involves parts of the brain that control thought, and memory. The gradual and irreversible neurological alignment known as Alzheimer’s disease affects millions of individuals worldwide. The correct identification of this issue can improve the quality of life and help create more efficient treatment alternatives. In this paper characteristics from MRI scans are automatically extracted and labeled as either having healthy brains or having neurological conditions. In this paper, we proposed PCA for feature extraction, and after that data augmentation technique was used to increase input datasets, finally 2DCNN was used for classification. This approach promises better results compared to existing methods.