185 related articles for article (PubMed ID: 38159396)
1. Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model.
Shoaib MR; Emara HM; Zhao J; El-Shafai W; Soliman NF; Mubarak AS; Omer OA; El-Samie FEA; Esmaiel H
Comput Biol Med; 2024 Feb; 169():107834. PubMed ID: 38159396
[TBL] [Abstract][Full Text] [Related]
2. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.
Hassan D; Gill HM; Happe M; Bhatwadekar AD; Hajrasouliha AR; Janga SC
Front Med (Lausanne); 2022; 9():1050436. PubMed ID: 36425113
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. An automated unsupervised deep learning-based approach for diabetic retinopathy detection.
Naz H; Nijhawan R; Ahuja NJ
Med Biol Eng Comput; 2022 Dec; 60(12):3635-3654. PubMed ID: 36274090
[TBL] [Abstract][Full Text] [Related]
5. An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images.
Akella PL; Kumar R
Graefes Arch Clin Exp Ophthalmol; 2024 Jan; 262(1):231-247. PubMed ID: 37548671
[TBL] [Abstract][Full Text] [Related]
6. Micro-segmentation of retinal image lesions in diabetic retinopathy using energy-based fuzzy C-Means clustering (EFM-FCM).
Naz H; Nijhawan R; Ahuja NJ; Saba T; Alamri FS; Rehman A
Microsc Res Tech; 2024 Jan; 87(1):78-94. PubMed ID: 37681440
[TBL] [Abstract][Full Text] [Related]
7. MediDRNet: Tackling category imbalance in diabetic retinopathy classification with dual-branch learning and prototypical contrastive learning.
Teng S; Wang B; Yang F; Yi X; Zhang X; Sun Y
Comput Methods Programs Biomed; 2024 Aug; 253():108230. PubMed ID: 38810377
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.
Mohanty C; Mahapatra S; Acharya B; Kokkoras F; Gerogiannis VC; Karamitsos I; Kanavos A
Sensors (Basel); 2023 Jun; 23(12):. PubMed ID: 37420891
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis.
Islam MM; Yang HC; Poly TN; Jian WS; Jack Li YC
Comput Methods Programs Biomed; 2020 Jul; 191():105320. PubMed ID: 32088490
[TBL] [Abstract][Full Text] [Related]
14. Contrastive self-supervised learning for diabetic retinopathy early detection.
Ouyang J; Mao D; Guo Z; Liu S; Xu D; Wang W
Med Biol Eng Comput; 2023 Sep; 61(9):2441-2452. PubMed ID: 37119374
[TBL] [Abstract][Full Text] [Related]
15. Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.
Wu JH; Liu TYA; Hsu WT; Ho JH; Lee CC
J Med Internet Res; 2021 Jul; 23(7):e23863. PubMed ID: 34407500
[TBL] [Abstract][Full Text] [Related]
16. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study.
Ruamviboonsuk P; Tiwari R; Sayres R; Nganthavee V; Hemarat K; Kongprayoon A; Raman R; Levinstein B; Liu Y; Schaekermann M; Lee R; Virmani S; Widner K; Chambers J; Hersch F; Peng L; Webster DR
Lancet Digit Health; 2022 Apr; 4(4):e235-e244. PubMed ID: 35272972
[TBL] [Abstract][Full Text] [Related]
17. Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema- A Review.
Suriyasekeran K; Santhanamahalingam S; Duraisamy M
Adv Exp Med Biol; 2021; 1307():357-373. PubMed ID: 32166636
[TBL] [Abstract][Full Text] [Related]
18. Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases.
Abbas Q; Albathan M; Altameem A; Almakki RS; Hussain A
Diagnostics (Basel); 2023 Oct; 13(20):. PubMed ID: 37891986
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.
Nazir T; Nawaz M; Rashid J; Mahum R; Masood M; Mehmood A; Ali F; Kim J; Kwon HY; Hussain A
Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450729
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
