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Journal Abstract Search

613 related items for PubMed ID: 34033015

  • 1. Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
    Gayathri S, Gopi VP, Palanisamy P.
    Phys Eng Sci Med; 2021 Sep; 44(3):639-653. PubMed ID: 34033015
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  • 4. Automated classification of diabetic retinopathy through reliable feature selection.
    Gayathri S, Gopi VP, Palanisamy P.
    Phys Eng Sci Med; 2020 Sep; 43(3):927-945. PubMed ID: 32648111
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  • 6. An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.
    Papadopoulos A, Topouzis F, Delopoulos A.
    Sci Rep; 2021 Jul 12; 11(1):14326. PubMed ID: 34253799
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  • 7. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN.
    Durai DBJ, Jaya T.
    Med Biol Eng Comput; 2023 Aug 12; 61(8):2091-2113. PubMed ID: 37338737
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  • 8. MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning.
    Teng S, Wang B, Yang F, Yi X, Zhang X, Sun Y.
    Comput Methods Programs Biomed; 2024 Aug 12; 253():108230. PubMed ID: 38810377
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  • 9. Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network.
    Deepa V, Sathish Kumar C, Cherian T.
    Phys Eng Sci Med; 2022 Jun 12; 45(2):623-635. PubMed ID: 35587313
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  • 10. Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.
    Wu Z, Shi G, Chen Y, Shi F, Chen X, Coatrieux G, Yang J, Luo L, Li S.
    Artif Intell Med; 2020 Aug 12; 108():101936. PubMed ID: 32972665
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  • 11. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.
    Liu YP, Li Z, Xu C, Li J, Liang R.
    Artif Intell Med; 2019 Aug 12; 99():101694. PubMed ID: 31606108
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  • 12. Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients.
    Maqsood S, Damaševičius R, Maskeliūnas R.
    Sensors (Basel); 2021 Jun 03; 21(11):. PubMed ID: 34205120
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  • 14. Automated Identification of Diabetic Retinopathy Using Deep Learning.
    Gargeya R, Leng T.
    Ophthalmology; 2017 Jul 03; 124(7):962-969. PubMed ID: 28359545
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  • 15. Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images.
    Islam MR, Abdulrazak LF, Nahiduzzaman M, Goni MOF, Anower MS, Ahsan M, Haider J, Kowalski M.
    Comput Biol Med; 2022 Jul 03; 146():105602. PubMed ID: 35569335
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  • 17. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.
    Fu Y, Wei Y, Chen S, Chen C, Zhou R, Li H, Qiu M, Xie J, Huang D.
    Phys Med Biol; 2024 Feb 12; 69(4):. PubMed ID: 38271723
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  • 18. A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric.
    Chilukoti SV, Shan L, Tida VS, Maida AS, Hei X.
    BMC Med Inform Decis Mak; 2024 Feb 06; 24(1):37. PubMed ID: 38321416
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  • 19. Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.
    Albadr MAA, Ayob M, Tiun S, Al-Dhief FT, Hasan MK.
    Front Public Health; 2022 Feb 06; 10():925901. PubMed ID: 35979449
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  • 20. A deep learning framework for the early detection of multi-retinal diseases.
    Ejaz S, Baig R, Ashraf Z, Alnfiai MM, Alnahari MM, Alotaibi RM.
    PLoS One; 2024 Feb 06; 19(7):e0307317. PubMed ID: 39052616
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