Academic Journal
Retinal OCT image classification based on MGR-GAN.
| Τίτλος: | Retinal OCT image classification based on MGR-GAN. |
|---|---|
| Συγγραφείς: | Peng K; School of Automation and Information Engineering, Sichuan University of Science & Engineering, Key Laboratory of Artificial Intelligence, Yibin, 644000, Sichuan, China., Huang D; School of Automation and Information Engineering, Sichuan University of Science & Engineering, Key Laboratory of Artificial Intelligence, Yibin, 644000, Sichuan, China. Danhuang81@gmail.com., Chen Y; School of Automation and Information Engineering, Sichuan University of Science & Engineering, Key Laboratory of Artificial Intelligence, Yibin, 644000, Sichuan, China. |
| Πηγή: | Medical & biological engineering & computing [Med Biol Eng Comput] 2025 Jun; Vol. 63 (6), pp. 1749-1763. Date of Electronic Publication: 2025 Jan 25. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York, NY : Springer Original Publication: Stevenage, Eng., Peregrinus. |
| Ιατρικοί όροι (MeSH): | Tomography, Optical Coherence*/methods , Retina*/diagnostic imaging , Image Processing, Computer-Assisted*/methods , Neural Networks, Computer*, Humans ; Algorithms |
| Περίληψη: | Accurately classifying optical coherence tomography (OCT) images is essential for diagnosing and treating ophthalmic diseases. This paper introduces a novel generative adversarial network framework called MGR-GAN. The masked image modeling (MIM) method is integrated into the GAN model's generator, enhancing its ability to synthesize more realistic images by reconstructing them based on unmasked patches. A ResNet-structured discriminator is employed to determine whether the image is generated by the generator. Through the unique game process of the generative adversarial network (GAN) model, the discriminator acquires high-level discriminant features, essential for precise OCT classification. Experimental results demonstrate that MGR-GAN achieves a classification accuracy of 98.4% on the original UCSD dataset. As the trained generator can synthesize OCT images with higher precision, and owing to category imbalances in the UCSD dataset, the generated OCT images are leveraged to address this imbalance. After balancing the UCSD dataset, the classification accuracy further improves to 99%. (© 2025. International Federation for Medical and Biological Engineering.) |
| Competing Interests: | Declarations. Human rights statement: The included human study has been approved and performed in accordance with ethical standards. Informed consent: Informed consent was applied. Conflict of interest: The authors declare no competing interests. |
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| Grant Information: | 2022YFSY0056) This research was supported by the Key Research and Development Program of Sichuan Province |
| Contributed Indexing: | Keywords: Data imbalance; Generative adversarial networks; Image classification; Masked self-encoder; Optical coherence tomography |
| Entry Date(s): | Date Created: 20250125 Date Completed: 20250526 Latest Revision: 20250526 |
| Update Code: | 20260130 |
| DOI: | 10.1007/s11517-025-03286-1 |
| PMID: | 39862318 |
| Βάση Δεδομένων: | MEDLINE |
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