BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

156 related articles for article (PubMed ID: 37903967)

  • 21. Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy.
    Akram MU; Tariq A; Anjum MA; Javed MY
    Appl Opt; 2012 Jul; 51(20):4858-66. PubMed ID: 22781265
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).
    Noor-Ul-Huda M; Tehsin S; Ahmed S; Niazi FAK; Murtaza Z
    Biomed Tech (Berl); 2019 May; 64(3):297-307. PubMed ID: 30055096
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Weighted ensemble based automatic detection of exudates in fundus photographs.
    Prentasic P; Loncaric S
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():138-41. PubMed ID: 25569916
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Automatic detection of neovascularization on optic disk region with feature extraction and support vector machine.
    Shuang Yu ; Di Xiao ; Kanagasingam Y
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():1324-1327. PubMed ID: 28268569
    [TBL] [Abstract][Full Text] [Related]  

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

  • 26. Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening.
    Wang H; Yuan G; Zhao X; Peng L; Wang Z; He Y; Qu C; Peng Z
    Comput Methods Programs Biomed; 2020 Jul; 191():105398. PubMed ID: 32092614
    [TBL] [Abstract][Full Text] [Related]  

  • 27. 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; 99():101701. PubMed ID: 31606116
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Automatic detection of microaneurysms in retinal fundus images.
    Wu B; Zhu W; Shi F; Zhu S; Chen X
    Comput Med Imaging Graph; 2017 Jan; 55():106-112. PubMed ID: 27595214
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods.
    Cao W; Czarnek N; Shan J; Li L
    IEEE Trans Nanobioscience; 2018 Jul; 17(3):191-198. PubMed ID: 29994317
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention.
    Gu Z; Li Y; Wang Z; Kan J; Shu J; Wang Q
    Comput Intell Neurosci; 2023; 2023():1305583. PubMed ID: 36636467
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Detection of Hard Exudates Using Evolutionary Feature Selection in Retinal Fundus Images.
    Kadan AB; Subbian PS
    J Med Syst; 2019 May; 43(7):209. PubMed ID: 31144041
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Analysis on diagnosing diabetic retinopathy by segmenting blood vessels, optic disc and retinal abnormalities.
    Jadhav AS; Patil PB; Biradar S
    J Med Eng Technol; 2020 Aug; 44(6):299-316. PubMed ID: 32729345
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Retinal image assessment using bi-level adaptive morphological component analysis.
    Javidi M; Harati A; Pourreza H
    Artif Intell Med; 2019 Aug; 99():101702. PubMed ID: 31606110
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Automated Identification of Diabetic Retinopathy Using Deep Learning.
    Gargeya R; Leng T
    Ophthalmology; 2017 Jul; 124(7):962-969. PubMed ID: 28359545
    [TBL] [Abstract][Full Text] [Related]  

  • 35. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 36. 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; 10():925901. PubMed ID: 35979449
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Retinal image analysis for disease screening through local tetra patterns.
    Porwal P; Pachade S; Kokare M; Giancardo L; Mériaudeau F
    Comput Biol Med; 2018 Nov; 102():200-210. PubMed ID: 30308336
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy.
    Jelinek HF; Cree MJ; Leandro JJ; Soares JV; Cesar RM; Luckie A
    J Opt Soc Am A Opt Image Sci Vis; 2007 May; 24(5):1448-56. PubMed ID: 17429492
    [TBL] [Abstract][Full Text] [Related]  

  • 39. 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; 146():105602. PubMed ID: 35569335
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features.
    Mohan NJ; Murugan R; Goel T; Roy P
    J Digit Imaging; 2022 Jun; 35(3):496-513. PubMed ID: 35141807
    [TBL] [Abstract][Full Text] [Related]  

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