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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

105 related articles for article (PubMed ID: 27634547)

  • 1. Local characterization of neovascularization and identification of proliferative diabetic retinopathy in retinal fundus images.
    Gupta G; Kulasekaran S; Ram K; Joshi N; Sivaprakasam M; Gandhi R
    Comput Med Imaging Graph; 2017 Jan; 55():124-132. PubMed ID: 27634547
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Computer-assisted identification of proliferative diabetic retinopathy in color retinal images.
    Gupta G; Kulasekaran S; Ram K; Joshi N; Sivaprakasam M; Gandhi R
    Annu Int Conf IEEE Eng Med Biol Soc; 2015 Aug; 2015():5642-5. PubMed ID: 26737572
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
    Liu Q; Zou B; Chen J; Ke W; Yue K; Chen Z; Zhao G
    Comput Med Imaging Graph; 2017 Jan; 55():78-86. PubMed ID: 27665058
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Analysis of retinal fundus images for grading of diabetic retinopathy severity.
    Ahmad Fadzil MH; Izhar LI; Nugroho H; Nugroho HA
    Med Biol Eng Comput; 2011 Jun; 49(6):693-700. PubMed ID: 21271293
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine Learning Based Automatic Neovascularization Detection on Optic Disc Region.
    Yu S; Xiao D; Kanagasingam Y
    IEEE J Biomed Health Inform; 2018 May; 22(3):886-894. PubMed ID: 29727291
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification.
    Welikala RA; Dehmeshki J; Hoppe A; Tah V; Mann S; Williamson TH; Barman SA
    Comput Methods Programs Biomed; 2014 May; 114(3):247-61. PubMed ID: 24636803
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. Retinal Disease Screening Through Local Binary Patterns.
    Morales S; Engan K; Naranjo V; Colomer A
    IEEE J Biomed Health Inform; 2017 Jan; 21(1):184-192. PubMed ID: 26469792
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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; 316(22):2402-2410. PubMed ID: 27898976
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automated lesion detectors in retinal fundus images.
    Figueiredo IN; Kumar S; Oliveira CM; Ramos JD; Engquist B
    Comput Biol Med; 2015 Nov; 66():47-65. PubMed ID: 26378502
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis.
    Gegundez-Arias ME; Marin D; Ponte B; Alvarez F; Garrido J; Ortega C; Vasallo MJ; Bravo JM
    Comput Biol Med; 2017 Sep; 88():100-109. PubMed ID: 28711766
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.
    Khojasteh P; Aliahmad B; Kumar DK
    BMC Ophthalmol; 2018 Nov; 18(1):288. PubMed ID: 30400869
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Proliferative diabetic retinopathy characterization based on fractal features: Evaluation on a publicly available dataset.
    Orlando JI; van Keer K; Barbosa Breda J; Manterola HL; Blaschko MB; Clausse A
    Med Phys; 2017 Dec; 44(12):6425-6434. PubMed ID: 29044550
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Detection of neovascularization in retinal images using multivariate m-Mediods based classifier.
    Usman Akram M; Khalid S; Tariq A; Younus Javed M
    Comput Med Imaging Graph; 2013; 37(5-6):346-57. PubMed ID: 23916066
    [TBL] [Abstract][Full Text] [Related]  

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

  • 18. Automated detection of neovascularization for proliferative diabetic retinopathy screening.
    Roychowdhury S; Koozekanani DD; Parhi KK
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():1300-1303. PubMed ID: 28268564
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.
    Dupas B; Walter T; Erginay A; Ordonez R; Deb-Joardar N; Gain P; Klein JC; Massin P
    Diabetes Metab; 2010 Jun; 36(3):213-20. PubMed ID: 20219404
    [TBL] [Abstract][Full Text] [Related]  

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

    [Next]    [New Search]
    of 6.