Identification of Mild Cognitive Impairment Dementia

Abstract

Mild Cognitive Impairment (MCI) [1] is an early stage of Alzheimer’s disease (AD) with increased growth rate around the globe. It effects human’s cognitive ability and memory. It has no cure, once a person is affected. However, if early symptoms are diagnosed timely then it can be avoided or its growth rate could be slow down using medication. An early symptoms indication of AD is called Mild Cognitive Impairment (MCI). Lack of distinct medical cure for this disease have urged the need to identify factors of early diagnosis of AD using automated process. Its common symptoms include cognitive impairment, memory loss, difficulty in thinking and understanding words, and inability to identify family members. Moreover, lack of Beta proteins and CDR [(Clinical Dementia Rating)] code in MRI (Magnetic Resonance Imaging) scans are important Clinical data parameters [1]. Identification of MCI using MRI scans of ADNI dataset is less prone to errors as MRI generate quality medical images [1]. This project aims to diagnose MCI using CNN (Convolution Neural Network) applied on MRI scans [1]. This project will be implemented in Python programming language using PyCharm and will provide services through an online web-based Interface. Web interface will be built using FLASK framework, HTML, CSS and Bootstrap. Machine learning training will be performed using python machine learning libraries such as SICKIT Learn