Self-supervised category selective attention classifier network for diabetic macular edema classification.

Autor: Chavan S; SVKM'S NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India. phd.sachinchavan@gmail.com., Choubey N; SVKM'S NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India.
Jazyk: angličtina
Zdroj: Acta diabetologica [Acta Diabetol] 2024 Jul; Vol. 61 (7), pp. 879-896. Date of Electronic Publication: 2024 Mar 23.
DOI: 10.1007/s00592-024-02257-6
Abstrakt: Aims: This study aims to develop an advanced model for the classification of Diabetic Macular Edema (DME) using deep learning techniques. Specifically, the objective is to introduce a novel architecture, SSCSAC-Net, that leverages self-supervised learning and category-selective attention mechanisms to improve the precision of DME classification.
Methods: The proposed SSCSAC-Net integrates self-supervised learning to effectively utilize unlabeled data for learning robust features related to DME. Additionally, it incorporates a category-specific attention mechanism and a domain-specific layer into the ResNet-152 base architecture. The model is trained using an ensemble of unsupervised and supervised learning techniques. Benchmark datasets are utilized for testing the model's performance, ensuring its robustness and generalizability across different data distributions.
Results: Evaluation of the SSCSAC-Net on multiple datasets demonstrates its superior performance compared to existing techniques. The model achieves high accuracy, precision, and recall rates, with an accuracy of 98.7%, precision of 98.6%, and recall of 98.8%. Furthermore, the incorporation of self-supervised learning reduces the dependency on extensive labeled data, making the solution more scalable and cost-effective.
Conclusions: The proposed SSCSAC-Net represents a significant advancement in automated DME classification. By effectively using self-supervised learning and attention mechanisms, the model offers improved accuracy in identifying DME-related features within retinal images. Its robustness and generalizability across different datasets highlight its potential for clinical applications, providing a valuable tool for clinicians in diagnosing DME effectively.
(© 2024. Springer-Verlag Italia S.r.l., part of Springer Nature.)
Databáze: MEDLINE
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