ALZHEIMER'S STAGE PREDICTION USING MRI IMAGES

Abstract

Alzheimer’s is the most common type and cause of dementia (loss in cognitive skills) and it does lead to the death of the victim. Stages of Alzheimer’s Disease can be predicted by the use of Magnetic Resonance Imaging (MRI) images. The objective of this project is to develop image recognition system to predict stage of Alzheimer’s disease through MRI images. This report explains the techniques, methods and approaches used to predict the stage of Alzheimer’s disease such as image pre processing, features extraction, classification. The project uses the class ofdeep neural network known as Convolution Neural Network (CNN), Tensor flow library Keras to develop the software. The benefit ofusing CNN lies within its capability of adaptively learning spatial hierarchies offeatures using the backpropagation technique provided by the building blocks, such as convolution layers, pooling layers, and fully connected layers. After trials and errors, a suitable set of training parameters are defined and network structure that consist of 1 input layer, 2 hidden layers and 1 output layer with 69 input neurons, 324 neurons for both hidden layers and 38 neurons for output layer is created. The system first proceeds with the pre-process ofthe captured image with threshold, inverting and smoothing. Filtering, segmentation, resizing and features extraction are also performed in the process. Next, the feed forward process through the network is invoked to yield an output matrix. Based on the output matrix, the recognized character can be determined. This system is designed to customize the network for an individual user. With an accuracy of 95% we have also included recommendations and conclusions for future development and in the report.