AUTHOR=A Ahila , M Poongodi , Hamdi Mounir , Bourouis Sami , Rastislav Kulhanek , Mohmed Faizaan 

TITLE=Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.834032

DOI=10.3389/fpubh.2022.834032

ISSN=2296-2565

ABSTRACT=Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression cannot be on time if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importance for patient care and efficient treatment. Neuroimaging techniques aim to assist the physician in the diagnosis of brain disorder by using images. Positron Emission Tomography (PET) is a kind of neuroimaging technique employed to create 3D images of the brain. Due to many PET images, researchers attempted to develop a Computer-Aided Diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods use image processing techniques for preprocessing and attributes extraction and then develop a model or classifier to classify the brain images.
As a result, the retrieved features have a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on Convolutional Neural Network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients. The proposed approach is evaluated at the 18 FDG-PET images of 855 patients, including 635 normal control and 220 Alzheimer's disease patients from the ADNI database. The result showed that the proposed CAD system yields an accuracy of 96\%, a sensitivity of 96\%, and a specificity of 94\%, leading to splendid performance when related to the methods already in use that are specified in the literature