A modified deep learning method for Alzheimer's disease detection based on the facial submicroscopic features in mice.
Alzheimer’s disease detection
Deep learning method
Facial image
Submicroscopic features
Journal
Biomedical engineering online
ISSN: 1475-925X
Titre abrégé: Biomed Eng Online
Pays: England
ID NLM: 101147518
Informations de publication
Date de publication:
31 Oct 2024
31 Oct 2024
Historique:
received:
19
08
2024
accepted:
25
10
2024
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
1
11
2024
Statut:
epublish
Résumé
Alzheimer's disease (AD) is a chronic disease among people aged 65 and older. As the aging population continues to grow at a rapid pace, AD has emerged as a pressing public health issue globally. Early detection of the disease is important, because increasing evidence has illustrated that early diagnosis holds the key to effective treatment of AD. In this work, we developed and refined a multi-layer cyclic Residual convolutional neural network model, specifically tailored to identify AD-related submicroscopic characteristics in the facial images of mice. Our experiments involved classifying the mice into two distinct groups: a normal control group and an AD group. Compared with the other deep learning models, the proposed model achieved a better detection performance in the dataset of the mouse experiment. The accuracy, sensitivity, specificity and precision for AD identification with our proposed model were as high as 99.78%, 100%, 99.65% and 99.44%, respectively. Moreover, the heat maps of AD correlation in the facial images of the mice were acquired with the class activation mapping algorithm. It was proven that the facial images contained AD-related submicroscopic features. Consequently, through our mouse experiments, we validated the feasibility and accuracy of utilizing a facial image-based deep learning model for AD identification. Therefore, the present study suggests the potential of using facial images for AD detection in humans through deep learning-based methods.
Identifiants
pubmed: 39482695
doi: 10.1186/s12938-024-01305-0
pii: 10.1186/s12938-024-01305-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
109Subventions
Organisme : Key Research and Development Program of Gansu Province
ID : 23YFFA0010
Organisme : Science and Technology Major Special Program of Gansu Province
ID : 23ZDKA011
Informations de copyright
© 2024. The Author(s).
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