158 related articles for article (PubMed ID: 31906601)
21. A novel retinal vessel detection approach based on multiple deep convolution neural networks.
Guo Y; Budak Ü; Şengür A
Comput Methods Programs Biomed; 2018 Dec; 167():43-48. PubMed ID: 30501859
[TBL] [Abstract][Full Text] [Related]
22. Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion.
Prentašić P; Lončarić S
Comput Methods Programs Biomed; 2016 Dec; 137():281-292. PubMed ID: 28110732
[TBL] [Abstract][Full Text] [Related]
23. Fully automated detection of retinal disorders by image-based deep learning.
Li F; Chen H; Liu Z; Zhang X; Wu Z
Graefes Arch Clin Exp Ophthalmol; 2019 Mar; 257(3):495-505. PubMed ID: 30610422
[TBL] [Abstract][Full Text] [Related]
24. Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images.
Hu W; Zhang Y; Li L
Sensors (Basel); 2019 Aug; 19(16):. PubMed ID: 31426516
[TBL] [Abstract][Full Text] [Related]
25. Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks.
Joonyoung Song ; Boreom Lee
Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():681-684. PubMed ID: 29059964
[TBL] [Abstract][Full Text] [Related]
26. Hybrid machine learning architecture for automated detection and grading of retinal images for diabetic retinopathy.
Narayanan BN; Hardie RC; De Silva MS; Kueterman NK
J Med Imaging (Bellingham); 2020 May; 7(3):034501. PubMed ID: 32613029
[No Abstract] [Full Text] [Related]
27. Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine.
Saha SK; Fernando B; Cuadros J; Xiao D; Kanagasingam Y
J Digit Imaging; 2018 Dec; 31(6):869-878. PubMed ID: 29704086
[TBL] [Abstract][Full Text] [Related]
28. Comparison of smartphone-based retinal imaging systems for diabetic retinopathy detection using deep learning.
Karakaya M; Hacisoftaoglu RE
BMC Bioinformatics; 2020 Jul; 21(Suppl 4):259. PubMed ID: 32631221
[TBL] [Abstract][Full Text] [Related]
29. A data-driven approach to referable diabetic retinopathy detection.
Pires R; Avila S; Wainer J; Valle E; Abramoff MD; Rocha A
Artif Intell Med; 2019 May; 96():93-106. PubMed ID: 31164214
[TBL] [Abstract][Full Text] [Related]
30. Prediction of Diabetes through Retinal Images Using Deep Neural Network.
Ragab M; Al-Ghamdi ASA; Fakieh B; Choudhry H; Mansour RF; Koundal D
Comput Intell Neurosci; 2022; 2022():7887908. PubMed ID: 35694596
[TBL] [Abstract][Full Text] [Related]
31. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning.
Bajwa MN; Malik MI; Siddiqui SA; Dengel A; Shafait F; Neumeier W; Ahmed S
BMC Med Inform Decis Mak; 2019 Jul; 19(1):136. PubMed ID: 31315618
[TBL] [Abstract][Full Text] [Related]
32. Microaneurysm detection in fundus images using a two-step convolutional neural network.
Eftekhari N; Pourreza HR; Masoudi M; Ghiasi-Shirazi K; Saeedi E
Biomed Eng Online; 2019 May; 18(1):67. PubMed ID: 31142335
[TBL] [Abstract][Full Text] [Related]
33. Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading.
Zhou K; Gu Z; Liu W; Luo W; Cheng J; Gao S; Liu J
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():2724-2727. PubMed ID: 30440966
[TBL] [Abstract][Full Text] [Related]
34. Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.
Burlina PM; Joshi N; Pacheco KD; Liu TYA; Bressler NM
JAMA Ophthalmol; 2019 Mar; 137(3):258-264. PubMed ID: 30629091
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Development of a Deep Learning Algorithm for Automatic Diagnosis of Diabetic Retinopathy.
Raju M; Pagidimarri V; Barreto R; Kadam A; Kasivajjala V; Aswath A
Stud Health Technol Inform; 2017; 245():559-563. PubMed ID: 29295157
[TBL] [Abstract][Full Text] [Related]
37. Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy.
Mansour RF
Biomed Eng Lett; 2018 Feb; 8(1):41-57. PubMed ID: 30603189
[TBL] [Abstract][Full Text] [Related]
38. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.
Abbas Q; Fondon I; Sarmiento A; Jiménez S; Alemany P
Med Biol Eng Comput; 2017 Nov; 55(11):1959-1974. PubMed ID: 28353133
[TBL] [Abstract][Full Text] [Related]
39. Explainable Diabetic Retinopathy using EfficientNET
Chetoui M; Akhloufi MA
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1966-1969. PubMed ID: 33018388
[TBL] [Abstract][Full Text] [Related]
40. Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks.
Yu C; Xie S; Niu S; Ji Z; Fan W; Yuan S; Liu Q; Chen Q
Med Phys; 2019 Oct; 46(10):4502-4519. PubMed ID: 31315159
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]