168 related articles for article (PubMed ID: 28268569)
1. 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]
2. 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]
3. 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]
4. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.
Akyol K; Şen B; Bayır Ş
Comput Math Methods Med; 2016; 2016():6814791. PubMed ID: 27110272
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation.
Long S; Huang X; Chen Z; Pardhan S; Zheng D
Biomed Res Int; 2019; 2019():3926930. PubMed ID: 30809539
[TBL] [Abstract][Full Text] [Related]
7. Detection of neovascularization in the optic disc using an AM-FM representation, granulometry, and vessel segmentation.
Agurto C; Yu H; Murray V; Pattichis MS; Barriga S; Bauman W; Soliz P
Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():4946-9. PubMed ID: 23367037
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Controlled differential evolution based detection of neovascularization on optic disc using support vector machine.
Biswal B; P GP; Biswal PK
Biomed Tech (Berl); 2020 Aug; ():. PubMed ID: 32776891
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features.
Venkatesan R; Chandakkar P; Li B; Li HK
Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():1462-5. PubMed ID: 23366177
[TBL] [Abstract][Full Text] [Related]
14. Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.
Jaya T; Dheeba J; Singh NA
J Digit Imaging; 2015 Dec; 28(6):761-8. PubMed ID: 25822397
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.
Raja DS; Vasuki S
Comput Math Methods Med; 2015; 2015():419279. PubMed ID: 25810749
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Topographical distribution of retinal and optic disc neovascularization in early stages of proliferative diabetic retinopathy.
Jansson RW; Frøystein T; Krohn J
Invest Ophthalmol Vis Sci; 2012 Dec; 53(13):8246-52. PubMed ID: 23169887
[TBL] [Abstract][Full Text] [Related]
20. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.
Welikala RA; Fraz MM; Dehmeshki J; Hoppe A; Tah V; Mann S; Williamson TH; Barman SA
Comput Med Imaging Graph; 2015 Jul; 43():64-77. PubMed ID: 25841182
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]