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.
177 related articles for article (PubMed ID: 34330270)
1. Multi-label classification of fundus images based on graph convolutional network. Cheng Y; Ma M; Li X; Zhou Y BMC Med Inform Decis Mak; 2021 Jul; 21(Suppl 2):82. PubMed ID: 34330270 [TBL] [Abstract][Full Text] [Related]
2. Detection of retinal lesions in diabetic retinopathy: comparative evaluation of 7-field digital color photography versus red-free photography. Venkatesh P; Sharma R; Vashist N; Vohra R; Garg S Int Ophthalmol; 2015 Oct; 35(5):635-40. PubMed ID: 22961609 [TBL] [Abstract][Full Text] [Related]
3. Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning. Pan X; Jin K; Cao J; Liu Z; Wu J; You K; Lu Y; Xu Y; Su Z; Jiang J; Yao K; Ye J Graefes Arch Clin Exp Ophthalmol; 2020 Apr; 258(4):779-785. PubMed ID: 31932886 [TBL] [Abstract][Full Text] [Related]
4. A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection. AbdelMaksoud E; Barakat S; Elmogy M Comput Biol Med; 2020 Nov; 126():104039. PubMed ID: 33068807 [TBL] [Abstract][Full Text] [Related]
5. 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]
6. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Niemeijer M; van Ginneken B; Russell SR; Suttorp-Schulten MS; Abràmoff MD Invest Ophthalmol Vis Sci; 2007 May; 48(5):2260-7. PubMed ID: 17460289 [TBL] [Abstract][Full Text] [Related]
7. Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy. Romero-Oraá R; García M; Oraá-Pérez J; López-Gálvez MI; Hornero R Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33207825 [TBL] [Abstract][Full Text] [Related]
8. 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]
9. EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks. Wan C; Chen Y; Li H; Zheng B; Chen N; Yang W; Wang C; Li Y Dis Markers; 2021; 2021():6482665. PubMed ID: 34512815 [TBL] [Abstract][Full Text] [Related]
10. Detection of Fundus Lesions through a Convolutional Neural Network in Patients with Diabetic Retinopathy. Santos C; de Aguiar MS; Welfer D; Belloni BM Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():2692-2695. PubMed ID: 34891806 [TBL] [Abstract][Full Text] [Related]
11. A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model. Santos C; Aguiar M; Welfer D; Belloni B Sensors (Basel); 2022 Aug; 22(17):. PubMed ID: 36080898 [TBL] [Abstract][Full Text] [Related]
12. Automatic non-proliferative diabetic retinopathy screening system based on color fundus image. Xiao Z; Zhang X; Geng L; Zhang F; Wu J; Tong J; Ogunbona PO; Shan C Biomed Eng Online; 2017 Oct; 16(1):122. PubMed ID: 29073912 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Distinguising Proof of Diabetic Retinopathy Detection by Hybrid Approaches in Two Dimensional Retinal Fundus Images. S K; D M J Med Syst; 2019 May; 43(6):173. PubMed ID: 31069550 [TBL] [Abstract][Full Text] [Related]
16. Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images. Ramya J; Rajakumar MP; Maheswari BU J Digit Imaging; 2022 Feb; 35(1):56-67. PubMed ID: 34997375 [TBL] [Abstract][Full Text] [Related]
17. An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection. Ullah H; Saba T; Islam N; Abbas N; Rehman A; Mehmood Z; Anjum A Microsc Res Tech; 2019 Apr; 82(4):361-372. PubMed ID: 30677193 [TBL] [Abstract][Full Text] [Related]
18. Deep Bayesian baseline for segmenting diabetic retinopathy lesions: Advances and challenges. Garifullin A; Lensu L; Uusitalo H Comput Biol Med; 2021 Sep; 136():104725. PubMed ID: 34399196 [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. Secondary Observer System for Detection of Microaneurysms in Fundus Images Using Texture Descriptors. Derwin DJ; Selvi ST; Singh OJ J Digit Imaging; 2020 Feb; 33(1):159-167. PubMed ID: 31144148 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]