These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

159 related items for PubMed ID: 39173657

  • 21. A hybrid model for the detection of retinal disorders using artificial intelligence techniques.
    Salaheldin AM, Abdel Wahed M, Saleh N.
    Biomed Phys Eng Express; 2024 Jul 10; 10(5):. PubMed ID: 38955139
    [Abstract] [Full Text] [Related]

  • 22. DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition.
    Lisha LB, Helen Sulochana C.
    Med Biol Eng Comput; 2024 Sep 10; 62(9):2911-2938. PubMed ID: 38713340
    [Abstract] [Full Text] [Related]

  • 23. 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 Sep 10; 10():925901. PubMed ID: 35979449
    [Abstract] [Full Text] [Related]

  • 24. Deep long and short term memory based Red Fox optimization algorithm for diabetic retinopathy detection and classification.
    Pugal Priya R, Saradadevi Sivarani T, Gnana Saravanan A.
    Int J Numer Method Biomed Eng; 2022 Mar 10; 38(3):e3560. PubMed ID: 34865312
    [Abstract] [Full Text] [Related]

  • 25. Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening.
    Shah P, Mishra DK, Shanmugam MP, Doshi B, Jayaraj H, Ramanjulu R.
    Indian J Ophthalmol; 2020 Feb 10; 68(2):398-405. PubMed ID: 31957737
    [Abstract] [Full Text] [Related]

  • 26. Revolutionizing crop disease detection with computational deep learning: a comprehensive review.
    Ngugi HN, Ezugwu AE, Akinyelu AA, Abualigah L.
    Environ Monit Assess; 2024 Feb 24; 196(3):302. PubMed ID: 38401024
    [Abstract] [Full Text] [Related]

  • 27. 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
    [Abstract] [Full Text] [Related]

  • 28. Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.
    Wu JH, Liu TYA, Hsu WT, Ho JH, Lee CC.
    J Med Internet Res; 2021 Jul 03; 23(7):e23863. PubMed ID: 34407500
    [Abstract] [Full Text] [Related]

  • 29. Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms.
    Tsao HY, Chan PY, Su EC.
    BMC Bioinformatics; 2018 Aug 13; 19(Suppl 9):283. PubMed ID: 30367589
    [Abstract] [Full Text] [Related]

  • 30. Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets.
    Saini M, Susan S.
    Comput Biol Med; 2022 Oct 13; 149():105989. PubMed ID: 36037631
    [Abstract] [Full Text] [Related]

  • 31. Leveraging uncertainty information from deep neural networks for disease detection.
    Leibig C, Allken V, Ayhan MS, Berens P, Wahl S.
    Sci Rep; 2017 Dec 19; 7(1):17816. PubMed ID: 29259224
    [Abstract] [Full Text] [Related]

  • 32. CLAHE-CapsNet: Efficient retina optical coherence tomography classification using capsule networks with contrast limited adaptive histogram equalization.
    Opoku M, Weyori BA, Adekoya AF, Adu K.
    PLoS One; 2023 Dec 19; 18(11):e0288663. PubMed ID: 38032915
    [Abstract] [Full Text] [Related]

  • 33. CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.
    Wei H, Shi P, Miao J, Zhang M, Bai G, Qiu J, Liu F, Yuan W.
    Comput Biol Med; 2024 Jun 19; 175():108459. PubMed ID: 38701588
    [Abstract] [Full Text] [Related]

  • 34. DREAM: diabetic retinopathy analysis using machine learning.
    Roychowdhury S, Koozekanani DD, Parhi KK.
    IEEE J Biomed Health Inform; 2014 Sep 19; 18(5):1717-28. PubMed ID: 25192577
    [Abstract] [Full Text] [Related]

  • 35. Deep image mining for diabetic retinopathy screening.
    Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M.
    Med Image Anal; 2017 Jul 19; 39():178-193. PubMed ID: 28511066
    [Abstract] [Full Text] [Related]

  • 36. Medical image analysis using deep learning algorithms.
    Li M, Jiang Y, Zhang Y, Zhu H.
    Front Public Health; 2023 Jul 19; 11():1273253. PubMed ID: 38026291
    [Abstract] [Full Text] [Related]

  • 37.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 38. 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 19; 146():105602. PubMed ID: 35569335
    [Abstract] [Full Text] [Related]

  • 39.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 40. Understanding inherent image features in CNN-based assessment of diabetic retinopathy.
    Reguant R, Brunak S, Saha S.
    Sci Rep; 2021 May 06; 11(1):9704. PubMed ID: 33958686
    [Abstract] [Full Text] [Related]

    Page: [Previous] [Next] [New Search]
    of 8.