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Journal Abstract Search

366 related items for PubMed ID: 34896689

  • 1. Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images.
    Toğaçar M.
    Comput Methods Programs Biomed; 2022 Feb; 214():106579. PubMed ID: 34896689
    [Abstract] [Full Text] [Related]

  • 2. DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images.
    Raza M, Naveed K, Akram A, Salem N, Afaq A, Madni HA, Khan MAU, Din MZ.
    PLoS One; 2021 Feb; 16(12):e0261698. PubMed ID: 34972109
    [Abstract] [Full Text] [Related]

  • 3. Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
    Tsiknakis N, Theodoropoulos D, Manikis G, Ktistakis E, Boutsora O, Berto A, Scarpa F, Scarpa A, Fotiadis DI, Marias K.
    Comput Biol Med; 2021 Aug; 135():104599. PubMed ID: 34247130
    [Abstract] [Full Text] [Related]

  • 4. Automated detection of diabetic retinopathy using custom convolutional neural network.
    Albahli S, Ahmad Hassan Yar GN.
    J Xray Sci Technol; 2022 Aug; 30(2):275-291. PubMed ID: 35001904
    [Abstract] [Full Text] [Related]

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

  • 6. A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.
    S K S, P A.
    J Med Syst; 2017 Nov 09; 41(12):201. PubMed ID: 29124453
    [Abstract] [Full Text] [Related]

  • 7. 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 09; 99():101701. PubMed ID: 31606116
    [Abstract] [Full Text] [Related]

  • 8. Weakly supervised training for eye fundus lesion segmentation in patients with diabetic retinopathy.
    Li Y, Zhu M, Sun G, Chen J, Zhu X, Yang J.
    Math Biosci Eng; 2022 Mar 24; 19(5):5293-5311. PubMed ID: 35430865
    [Abstract] [Full Text] [Related]

  • 9. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR.
    JAMA; 2016 Dec 13; 316(22):2402-2410. PubMed ID: 27898976
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  • 12. An Intelligent Model for Blood Vessel Segmentation in Diagnosing DR Using CNN.
    Sangeethaa SN, Uma Maheswari P.
    J Med Syst; 2018 Aug 15; 42(10):175. PubMed ID: 30109508
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  • 14. Recent trends and advances in fundus image analysis: A review.
    Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ.
    Comput Biol Med; 2022 Dec 15; 151(Pt A):106277. PubMed ID: 36370579
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  • 18. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.
    Hassan D, Gill HM, Happe M, Bhatwadekar AD, Hajrasouliha AR, Janga SC.
    Front Med (Lausanne); 2022 Dec 15; 9():1050436. PubMed ID: 36425113
    [Abstract] [Full Text] [Related]

  • 19. Automatic Diagnosis of Diabetic Retinopathy from Fundus Images Using Neuro-Evolutionary Algorithms.
    Aquino-Brítez D, Gómez JA, Noguera JLV, García-Torres M, Román JCM, Gardel-Sotomayor PE, Benitez VEC, Matto IC, Pinto-Roa DP, Facon J, Grillo SA.
    Stud Health Technol Inform; 2022 Jun 06; 290():689-693. PubMed ID: 35673105
    [Abstract] [Full Text] [Related]

  • 20. Optic disc detection and segmentation using saliency mask in retinal fundus images.
    Zaaboub N, Sandid F, Douik A, Solaiman B.
    Comput Biol Med; 2022 Nov 06; 150():106067. PubMed ID: 36150251
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