142 related articles for article (PubMed ID: 38321416)
1. 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]
2. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification.
Fu Y; Wei Y; Chen S; Chen C; Zhou R; Li H; Qiu M; Xie J; Huang D
Phys Med Biol; 2024 Feb; 69(4):. PubMed ID: 38271723
[No Abstract] [Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. Automated Identification of Diabetic Retinopathy Using Deep Learning.
Gargeya R; Leng T
Ophthalmology; 2017 Jul; 124(7):962-969. PubMed ID: 28359545
[TBL] [Abstract][Full Text] [Related]
7. Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN.
Durai DBJ; Jaya T
Med Biol Eng Comput; 2023 Aug; 61(8):2091-2113. PubMed ID: 37338737
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis.
Cao P; Ren F; Wan C; Yang J; Zaiane O
Comput Med Imaging Graph; 2018 Nov; 69():112-124. PubMed ID: 30237145
[TBL] [Abstract][Full Text] [Related]
11. Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models.
Alam MN; Yamashita R; Ramesh V; Prabhune T; Lim JI; Chan RVP; Hallak J; Leng T; Rubin D
Sci Rep; 2023 Apr; 13(1):6047. PubMed ID: 37055475
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. 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]
15. 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]
16. Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.
Albadr MAA; Ayob M; Tiun S; Al-Dhief FT; Hasan MK
Front Public Health; 2022; 10():925901. PubMed ID: 35979449
[TBL] [Abstract][Full Text] [Related]
17. Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN.
Alwakid G; Gouda W; Humayun M
Diagnostics (Basel); 2023 Jul; 13(14):. PubMed ID: 37510123
[TBL] [Abstract][Full Text] [Related]
18. Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy.
Sayres R; Taly A; Rahimy E; Blumer K; Coz D; Hammel N; Krause J; Narayanaswamy A; Rastegar Z; Wu D; Xu S; Barb S; Joseph A; Shumski M; Smith J; Sood AB; Corrado GS; Peng L; Webster DR
Ophthalmology; 2019 Apr; 126(4):552-564. PubMed ID: 30553900
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Multi-scale multi-attention network for diabetic retinopathy grading.
Xia H; Long J; Song S; Tan Y
Phys Med Biol; 2023 Dec; 69(1):. PubMed ID: 38035368
[No Abstract] [Full Text] [Related]
[Next] [New Search]
