PMID- 35983158
OWN - NLM
STAT- MEDLINE
DCOM- 20220822
LR  - 20220822
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and 
      Convolutional Neural Network.
PG  - 7413081
LID - 10.1155/2022/7413081 [doi]
LID - 7413081
AB  - There is a wide variety of effects of Alzheimer's disease (AD), a 
      neurodegenerative disease that can lead to cognitive decline, deterioration of 
      daily life, and behavioral and psychological changes. A polymorphism of the ApoE 
      gene epsilon 4 is considered a genetic risk factor for Alzheimer's disease. The purpose 
      of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) 
      have a causal relationship with quantitative PET imaging traits. Additionally, 
      the classification of AD is based on the frequency of brain tissue variations in 
      PET images using a combination of k-nearest-neighbor (KNN), support vector 
      machine (SVM), linear discrimination analysis (LDA), and convolutional neural 
      network (CNN) techniques. According to the results, the suggested SNPs appear to 
      be associated with quantitative traits more strongly than the SNPs in the ApoE 
      genes. Regarding the classification result, the highest accuracy is obtained by 
      the CNN with 91.1%. These results indicate that the KNN and CNN methods are 
      beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with 
      a lower level of accuracy.
CI  - Copyright (c) 2022 Morteza Amini et al.
FAU - Amini, Morteza
AU  - Amini M
AUID- ORCID: 0000-0002-8033-9519
AD  - Department of Cognitive Modeling, Institute for Cognitive Science Studies, 
      Tehran, Iran.
FAU - Pedram, Mir Mohsen
AU  - Pedram MM
AUID- ORCID: 0000-0002-0674-4428
AD  - Department of Cognitive Modeling, Institute for Cognitive Science Studies, 
      Tehran, Iran.
AD  - Department of Electrical and Computer Engineering, Faculty of Engineering, 
      Kharazmi University, Tehran, Iran.
FAU - Moradi, AliReza
AU  - Moradi A
AUID- ORCID: 0000-0001-5351-6017
AD  - Department of Clinical Psychology, Faculty of Psychology and Educational Science, 
      Kharazmi University, Tehran, Iran.
AD  - Department of Cognitive Psychology, Institute for Cognitive Science Studies, 
      Tehran, Iran.
FAU - Jamshidi, Mahdieh
AU  - Jamshidi M
AUID- ORCID: 0000-0002-5657-6821
AD  - Department of Cognitive Modeling, Institute for Cognitive Science Studies, 
      Tehran, Iran.
FAU - Ouchani, Mahshad
AU  - Ouchani M
AUID- ORCID: 0000-0002-5019-6828
AD  - Department of Cognitive Psychology, Institute for Cognitive Science Studies, 
      Tehran, Iran.
LA  - eng
PT  - Journal Article
DEP - 20220809
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
RN  - 0 (Apolipoproteins E)
SB  - IM
MH  - *Alzheimer Disease/diagnostic imaging/genetics
MH  - Apolipoproteins E/genetics
MH  - Humans
MH  - Magnetic Resonance Imaging/methods
MH  - Neural Networks, Computer
MH  - *Neurodegenerative Diseases
MH  - Positron-Emission Tomography/methods
PMC - PMC9381254
COIS- The authors declare that they have no conflicts of interest to report regarding 
      the publication of this paper.
EDAT- 2022/08/20 06:00
MHDA- 2022/08/23 06:00
PMCR- 2022/08/09
CRDT- 2022/08/19 02:14
PHST- 2021/12/08 00:00 [received]
PHST- 2022/06/01 00:00 [revised]
PHST- 2022/06/10 00:00 [accepted]
PHST- 2022/08/19 02:14 [entrez]
PHST- 2022/08/20 06:00 [pubmed]
PHST- 2022/08/23 06:00 [medline]
PHST- 2022/08/09 00:00 [pmc-release]
AID - 10.1155/2022/7413081 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Aug 9;2022:7413081. doi: 10.1155/2022/7413081. 
      eCollection 2022.