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Journal Abstract Search

361 related items for PubMed ID: 35001904

  • 21. Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.
    Hassan B, Hassan T, Li B, Ahmed R, Hassan O.
    Sensors (Basel); 2019 Jul 05; 19(13):. PubMed ID: 31284442
    [Abstract] [Full Text] [Related]

  • 22. Neovascularization Detection and Localization in Fundus Images Using Deep Learning.
    Tang MCS, Teoh SS, Ibrahim H, Embong Z.
    Sensors (Basel); 2021 Aug 06; 21(16):. PubMed ID: 34450766
    [Abstract] [Full Text] [Related]

  • 23. Transfer learning-driven ensemble model for detection of diabetic retinopathy disease.
    Chaurasia BK, Raj H, Rathour SS, Singh PB.
    Med Biol Eng Comput; 2023 Aug 06; 61(8):2033-2049. PubMed ID: 37296285
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  • 24. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity.
    Vives-Boix V, Ruiz-Fernández D.
    Comput Methods Programs Biomed; 2021 Jul 06; 206():106094. PubMed ID: 34010801
    [Abstract] [Full Text] [Related]

  • 25. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.
    Nazir T, Nawaz M, Rashid J, Mahum R, Masood M, Mehmood A, Ali F, Kim J, Kwon HY, Hussain A.
    Sensors (Basel); 2021 Aug 05; 21(16):. PubMed ID: 34450729
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  • 26. AUTOMATIC DETECTION AND GRADING OF DIABETIC MACULAR EDEMA BASED ON A DEEP NEURAL NETWORK.
    Guo X, Lu X, Zhang B, Hu X, Che S.
    Retina; 2022 Jun 01; 42(6):1095-1102. PubMed ID: 35152245
    [Abstract] [Full Text] [Related]

  • 27. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.
    Gao Z, Jin K, Yan Y, Liu X, Shi Y, Ge Y, Pan X, Lu Y, Wu J, Wang Y, Ye J.
    Graefes Arch Clin Exp Ophthalmol; 2022 May 01; 260(5):1663-1673. PubMed ID: 35066704
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  • 29. A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model.
    Santos C, Aguiar M, Welfer D, Belloni B.
    Sensors (Basel); 2022 Aug 26; 22(17):. PubMed ID: 36080898
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  • 31. Detection of Fundus Lesions through a Convolutional Neural Network in Patients with Diabetic Retinopathy.
    Santos C, de Aguiar MS, Welfer D, Belloni BM.
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov 26; 2021():2692-2695. PubMed ID: 34891806
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  • 34. Interobserver agreement in the interpretation of single-field digital fundus images for diabetic retinopathy screening.
    Ruamviboonsuk P, Teerasuwanajak K, Tiensuwan M, Yuttitham K, Thai Screening for Diabetic Retinopathy Study Group.
    Ophthalmology; 2006 May 26; 113(5):826-32. PubMed ID: 16650679
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  • 35. A convolutional neural network for the screening and staging of diabetic retinopathy.
    Shaban M, Ogur Z, Mahmoud A, Switala A, Shalaby A, Abu Khalifeh H, Ghazal M, Fraiwan L, Giridharan G, Sandhu H, El-Baz AS.
    PLoS One; 2020 May 26; 15(6):e0233514. PubMed ID: 32569310
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  • 37. Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.
    Romero-Oraá R, Herrero-Tudela M, López MI, Hornero R, García M.
    Comput Methods Programs Biomed; 2024 Jun 26; 249():108160. PubMed ID: 38583290
    [Abstract] [Full Text] [Related]

  • 38. Identification and Prediction of Diabetic Macular Edema on Two-dimensional Fundus Images with Raphe Assessment and Deep Convolution Neural Networks.
    S K.
    Biomed Environ Sci; 2022 Dec 20; 35(12):1156-1160. PubMed ID: 36597297
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