Alzheimer’s disease (AD) is the most common chronic disease in the elderly, with a high incidence rate.[1] Accurate early-stage Alzheimer’s disease identification is essential for successful treatment and recovery. Owing to which a significant research challenge is the precise diagnosis of Alzheimer’s disease. Different researchers used various techniques to detect Alzheimer’s disease effectively however; these methods still have a lack of prediction accuracy. Deep learning has had significant success and gained popularity in the field of medical imaging in recent years. It has emerged as the method of choice for examining medical images and has drawn considerable interest in the identification of AD. When it comes to detecting AD, the deep model is more precise and effective than ordinary machine learning technologies. In this review paper, we compared various research methodologies focusing on early diagnosis of AD based on convolutional neural networks (ConvNets) with the usage of Positron Emission Tomography (PET) and magnetic resonance imaging (MRI). In comparison to networks trained with single-modal images, the network trained with multi-modal images performs better. The performance of the suggested strategy has been examined using the data set from the Alzheimer’s Disease Neuroimaging Initiative.

1.
Fan
Zhang
,
Zhenzhen
Li
,
Boyan
Zhang
,
Haishun
Dua
,
Binjie
Wang
and
Xinhong
Zhang
, “
Multi-modal deep learning model for auxiliary diagnosis of Alzheimer’s disease
”, 0925-2312/©
2019
Elsevier B.V
, .
2.
Xin
Bi
,
Xiangguo
Zhao
,
Hong
Huang
,
Deyang
Chen
and
Yuliang
Ma
, “Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine”,
Springer Science+Business Media, LLC, part of Springer Nature
2019
, .
3.
Xiuli
Bi
,
Shutong
Li
,
Bin
Xiao
,
Yu
Li
,
Guoyin
Wang
and
Xu
Ma
, “
Computer Aided Alzheimer’s Disease Diagnosis by An Unsupervised Deep Learning Technology
”,
NEUCOM 20664
2018
, .
4.
Farheen
Ramzan
,
Muhammad Usman Ghani
Khan
,
Asim
Rehmat
,
Sajid
Iqbal
,
Tanzila
Saba
,
Amjad
Rehman
and
Zahid
Mehmood
, “A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks”,
Springer Science+Business Media, LLC, part of Springer Nature
2019
, .
5.
Shuang shuang
Gao
,
Dimas
Lima
, “
A review of the application of deep learning in the detection of Alzheimer’s disease
”, 2666-3074/KeAi,
2021
.
6.
JananiVenugopalan
,
LiTong
,
Hamid Reza
Hassanzadeh
and
May D.
Wang
, “
Multimodal deep learning models for early detection of Alzheimer’s disease stage
,
Scientific Reports
2021
.
7.
Shaker
El-Sappagh
,
Tamer
Abuhmed
,
S.M. Riazul
Islam
,
Kyung Sup
Kwak
, “Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data”, 0925-2312/ 2020 The Authors.
Published by Elsevier B.V
, .
8.
Shangran
Qiu
,
Prajakta S.
Joshi
,
Matthew I.
Miller
,
Chonghua
Xue
,
Xiao
Zhou
,
Cody
Karjadi
,
Gary H.
Chang
,
Anant S.
Joshi
, “
Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification
”,
Brain-A Journal Of Neurology
(
2020
), doi:.
9.
Tianyi
Yana
,
Yonghao
Wanga
,
Zizheng
Wengb
,
Wenying
Duc
,
Tiantian
Liua
,
Duanduan
Chena
,
Xuesong
Lid
,
Jinglong
Wue
and
Ying
Hanc
, “Early-Stage Identification and Pathological Development of Alzheimer’s Disease Using Multimodal MRI”, ISSN 1387-2877/19/2019 –
IOS Press and the authors
, DOI .
10.
Samsuddin
Ahmed
,
Kyu Yeong
Cho
,
Jang Jae
Lee
,
Byeong C.
Kim
,
Goo-Rak
Kwon
,
Kun Ho
Lee
and
Ho Yub
Jung
"
Ensembles Of Patch-Based Classifiers For Diagnosis Of Alzheimer Diseases
",
2169
3536
,
2019
-IEEE, VOLUME 7, 2019, DOI .
11.
Chiyu
Feng
,
Ahmed
Elazab
,
Peng
Yang
,
Tianfu
Wang
,
Feng
Zhou
,
Huoyou
Hu
,
Xiaohua
Xiao
, and
Baiying
Lei
, (Senior Member, Ieee)
Deep Learning Framework For Alzheimer’s Disease Diagnosis Via 3d-Cnn And Fsbi-Lstm
,
2021
-IEEE, DOI .
12.
Huanhuan
Ji
,
Zhenbing
Liu
,
Wei
Qi
Yan Reinhard Klette Early Diagnosis of Alzheimer’s Disease Using Deep Learning ICCCV’19, June 15–18,
2019
,
Jeju Island, South Korea
, .
13.
Alzheimer’s Association
. 2016
Alzheimer’s disease facts and figures
.
Alzheimers Dement.
12
(
4
),
459
509
(
2016
).
14.
Patterson
,
C.
World Alzheimer Report 2018—The State of the Art of Dementia Research: New Frontiers. (
Alzheimer’s Disease, International (ADI
),
London
,
2018
).
15.
T. K.
Khan
, ‘
‘Introduction to Alzheimer’s disease biomarkers,’
;’ in
Biomarkers in Alzheimer’s Disease
, 1st ed.
New York, NY, USA: Academic
,
2016
, p.
13
.
16.
M.K.
Gurucharan
, "
Basic CNN Architecture: Explaining 5 Layers of Convolutional Neural Network
",
2022
, upGrad.
17.
Hongming
Lia
,
Mohamad
Habesa
,
David A.
Wolkb
,
Yong
Fana
, "A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal magnetic resonance imaging data",
2019
,
Elsevier
, .
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