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Journal Abstract Search

333 related items for PubMed ID: 35545738

  • 1.
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  • 2. Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.
    Alyoubi WL, Abulkhair MF, Shalash WM.
    Sensors (Basel); 2021 May 26; 21(11):. PubMed ID: 34073541
    [Abstract] [Full Text] [Related]

  • 3. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.
    Asiri N, Hussain M, Al Adel F, Alzaidi N.
    Artif Intell Med; 2019 Aug 26; 99():101701. PubMed ID: 31606116
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  • 4. Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning.
    Atcı ŞY, Güneş A, Zontul M, Arslan Z.
    Tomography; 2024 Feb 05; 10(2):215-230. PubMed ID: 38393285
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  • 5. 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 Feb 05; 15(6):e0233514. PubMed ID: 32569310
    [Abstract] [Full Text] [Related]

  • 6. A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection.
    AbdelMaksoud E, Barakat S, Elmogy M.
    Comput Biol Med; 2020 Nov 05; 126():104039. PubMed ID: 33068807
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  • 8. 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 05; 99():101694. PubMed ID: 31606108
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  • 10. DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.
    Araújo T, Aresta G, Mendonça L, Penas S, Maia C, Carneiro Â, Mendonça AM, Campilho A.
    Med Image Anal; 2020 Jul 05; 63():101715. PubMed ID: 32434128
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  • 13. Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.
    Mohanty C, Mahapatra S, Acharya B, Kokkoras F, Gerogiannis VC, Karamitsos I, Kanavos A.
    Sensors (Basel); 2023 Jun 19; 23(12):. PubMed ID: 37420891
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  • 15. An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images.
    Akella PL, Kumar R.
    Graefes Arch Clin Exp Ophthalmol; 2024 Jan 19; 262(1):231-247. PubMed ID: 37548671
    [Abstract] [Full Text] [Related]

  • 16. A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification.
    Jiang H, Xu J, Shi R, Yang K, Zhang D, Gao M, Ma H, Qian W.
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul 19; 2020():1560-1563. PubMed ID: 33018290
    [Abstract] [Full Text] [Related]

  • 17. A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet.
    Vijayan M, S V.
    Diagnostics (Basel); 2023 Feb 17; 13(4):. PubMed ID: 36832262
    [Abstract] [Full Text] [Related]

  • 18. 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 17; 61(8):2033-2049. PubMed ID: 37296285
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  • 19. 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 17; 249():108160. PubMed ID: 38583290
    [Abstract] [Full Text] [Related]

  • 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 Jun 17; 19(7):e0307317. PubMed ID: 39052616
    [Abstract] [Full Text] [Related]

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