139 related articles for article (PubMed ID: 36516580)
1. Category weighted network and relation weighted label for diabetic retinopathy screening.
Han Z; Yang B; Deng S; Li Z; Tong Z
Comput Biol Med; 2023 Jan; 152():106408. PubMed ID: 36516580
[TBL] [Abstract][Full Text] [Related]
2. A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric.
Chilukoti SV; Shan L; Tida VS; Maida AS; Hei X
BMC Med Inform Decis Mak; 2024 Feb; 24(1):37. PubMed ID: 38321416
[TBL] [Abstract][Full Text] [Related]
3. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning.
Sugeno A; Ishikawa Y; Ohshima T; Muramatsu R
Comput Biol Med; 2021 Oct; 137():104795. PubMed ID: 34488028
[TBL] [Abstract][Full Text] [Related]
4. Handheld fundus camera performance, image quality and outcomes of diabetic retinopathy grading in a pilot screening study.
Kubin AM; Wirkkala J; Keskitalo A; Ohtonen P; Hautala N
Acta Ophthalmol; 2021 Dec; 99(8):e1415-e1420. PubMed ID: 33724706
[TBL] [Abstract][Full Text] [Related]
5. 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; 175():108459. PubMed ID: 38701588
[TBL] [Abstract][Full Text] [Related]
6. Agreement and Diagnostic Test Accuracy on Grading Diabetic Retinopathy Using Fundus Photographs by Allied Medical Personnel at a Community Diabetic Retinopathy Screening Program in Nepal.
Thapa R; Bajimaya S; Pradhan E; Sharma S; Kshetri BB; Paudel M; Paudyal G
Ophthalmic Epidemiol; 2021 Dec; 28(6):509-515. PubMed ID: 33502930
[No Abstract] [Full Text] [Related]
7. CRA-Net: Transformer guided category-relation attention network for diabetic retinopathy grading.
Zang F; Ma H
Comput Biol Med; 2024 Mar; 170():107993. PubMed ID: 38277925
[TBL] [Abstract][Full Text] [Related]
8. DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.
Araújo T; Aresta G; Mendonça L; Penas S; Maia C; Carneiro Â; Mendonça AM; Campilho A
Med Image Anal; 2020 Jul; 63():101715. PubMed ID: 32434128
[TBL] [Abstract][Full Text] [Related]
9. Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
Gayathri S; Gopi VP; Palanisamy P
Phys Eng Sci Med; 2021 Sep; 44(3):639-653. PubMed ID: 34033015
[TBL] [Abstract][Full Text] [Related]
10. A convolutional neural network for the screening and staging of diabetic retinopathy.
Shaban M; Ogur Z; Mahmoud A; Switala A; Shalaby A; Abu Khalifeh H; Ghazal M; Fraiwan L; Giridharan G; Sandhu H; El-Baz AS
PLoS One; 2020; 15(6):e0233514. PubMed ID: 32569310
[TBL] [Abstract][Full Text] [Related]
11. A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification.
Jiang H; Xu J; Shi R; Yang K; Zhang D; Gao M; Ma H; Qian W
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1560-1563. PubMed ID: 33018290
[TBL] [Abstract][Full Text] [Related]
12. Non-uniform Label Smoothing for Diabetic Retinopathy Grading from Retinal Fundus Images with Deep Neural Networks.
Galdran A; Chelbi J; Kobi R; Dolz J; Lombaert H; Ben Ayed I; Chakor H
Transl Vis Sci Technol; 2020 Jun; 9(2):34. PubMed ID: 32832207
[TBL] [Abstract][Full Text] [Related]
13. Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning.
Alyoubi WL; Abulkhair MF; Shalash WM
Sensors (Basel); 2021 May; 21(11):. PubMed ID: 34073541
[TBL] [Abstract][Full Text] [Related]
14. A novel approach for intelligent diagnosis and grading of diabetic retinopathy.
Hai Z; Zou B; Xiao X; Peng Q; Yan J; Zhang W; Yue K
Comput Biol Med; 2024 Apr; 172():108246. PubMed ID: 38471350
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.
Wu Z; Shi G; Chen Y; Shi F; Chen X; Coatrieux G; Yang J; Luo L; Li S
Artif Intell Med; 2020 Aug; 108():101936. PubMed ID: 32972665
[TBL] [Abstract][Full Text] [Related]
17. Development of revised ResNet-50 for diabetic retinopathy detection.
Lin CL; Wu KC
BMC Bioinformatics; 2023 Apr; 24(1):157. PubMed ID: 37076790
[TBL] [Abstract][Full Text] [Related]
18. Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy.
Tseng VS; Chen CL; Liang CM; Tai MC; Liu JT; Wu PY; Deng MS; Lee YW; Huang TY; Chen YH
Transl Vis Sci Technol; 2020 Jul; 9(2):41. PubMed ID: 32855845
[TBL] [Abstract][Full Text] [Related]
19. URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.
Yang K; Lu Y; Xue L; Yang Y; Chang S; Zhou C
Exp Biol Med (Maywood); 2023 Jun; 248(11):909-921. PubMed ID: 37466156
[TBL] [Abstract][Full Text] [Related]
20. Triple-DRNet: A triple-cascade convolution neural network for diabetic retinopathy grading using fundus images.
Jian M; Chen H; Tao C; Li X; Wang G
Comput Biol Med; 2023 Mar; 155():106631. PubMed ID: 36805216
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]