A novel approach for automatic classification of macular degeneration OCT images.
Age-related macular degeneration (AMD)
Attention mechanism
Automatic classification
Diabetic macular edema (DME)
Multi-scale structure
Optical coherence tomography (OCT)
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 08 2024
20 08 2024
Historique:
received:
14
05
2024
accepted:
13
08
2024
medline:
21
8
2024
pubmed:
21
8
2024
entrez:
20
8
2024
Statut:
epublish
Résumé
Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.
Identifiants
pubmed: 39164445
doi: 10.1038/s41598-024-70175-2
pii: 10.1038/s41598-024-70175-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
19285Subventions
Organisme : 2023 Hunan Traditional Chinese Medicine Scientific Research Project
ID : A2023048
Organisme : Research Foundation of Education Bureau of Hunan Province, China
ID : 23A0273
Organisme : Hunan Provincial Natural Science Foundation of China
ID : 2021JJ30173
Informations de copyright
© 2024. The Author(s).
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