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

Abstract

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.

RETRACTED: SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising

“…We investigate the setting for the adversary network for the poison attack, the assumption, the first case, taken as perfect knowledge gained by the adversary on the target classifier ( P TC ) and known feature space t ( x ).The second case, is that adversaries gained the less or limited knowledge ( L TC ), target classifier. We assumed that attacker may have knowledge of features representation, but not the training dataset (Rathore et al, 2022; Poongodi, Bourouis et al, 2022; Ramesh, Lihore et al, 2022; Poongodi, Malviya, Hamdi et al, 2022; Poongodi, Malviya, Kumar et al, 2022; Poongodi, Hamdi, & Wang 2022; Poongodi et al, 2021; Ramesh, Vijayaragavan et al, 2022; Hamdi et al, 2022; Poongodi, Hamdi, Malviya et al, 2022; Kamruzzaman 2021; Hossain et al, 2022; Chen et al, 2019; Kamruzzaman 2013, 2014; Zhang et al, 2021; Hossain, Kamruzzaman et al, 2022; Sarker et al, 2021; Shi et al, 2020; Chen et al, 2020).…”

Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain

“…Most of these studies have used structural imaging of the brain, with few studies using functional imaging, specifically 18F-FDG-PET. Some researchers have attempted to analyze 18F-FDG-PET for AD predictions, but these studies have yielded limited success (Liu et al, 2018 ; Lu et al, 2018 ; Pan et al, 2018 ; Ding et al, 2019 ; Huang et al, 2019 ; Hamdi et al, 2022 ). Tables 4 , 5 summarize state-of-the-art deep learning methods for prediction of AD using 18F-FDG-PET imaging.…”

Deep Learning Aided Neuroimaging and Brain Regulation

“…One example of deep learning-assisted neuroimaging is the use of convolutional neural networks (CNNs) to improve the accuracy of PET image segmentation. In one study, the researchers developed a CNN-based segmentation method that achieved higher accuracy (96%), sensitivity (96%), and specificity (94%) than the traditional methods in the evaluation of neuro images for the diagnosis of Alzheimer's disease, which was evaluated using the 18 FDG-PET images of 855 patients including 635 normal control and 220 Alzheimer's disease patients from the ADNI database, thus capable of discriminating the normal control from the Alzheimer's disease patients [86].…”