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

Frontiers in Public Health

article

Faizaan Mohmed¹  Ahila A²  Poongodi M³  Mounir Hamdi³  Sami Bourouis⁴  Kulhanek Rastislav⁵ 

[1] School of Creative Tech, University of Bolton;Department of Electronics and Communication Engineering, Sethu Institute of Technology;College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation;Department of Information Technology, College of Computers and Information Technology, Taif University;Information Systems Department, Faculty of Management, Comenius University in Bratislava

关键词: Alzheimer's disease;    accuracy;    convolutional neural network;    deep learning;    feature extraction;    image analysis;    image classification and positron emission tomography;   

DOI  :  10.3389/fpubh.2022.834032

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed 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 disorders 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 computer-aided diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods used image processing techniques for preprocessing and attributes extraction and then developed a model or classifier to classify the brain images. As a result, the retrieved features had a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on a 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 using the 18FDG-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.

【 授权许可】

CC BY