135 related articles for article (PubMed ID: 37510123)
1. 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]
2. Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement.
Alwakid G; Gouda W; Humayun M
Healthcare (Basel); 2023 Mar; 11(6):. PubMed ID: 36981520
[TBL] [Abstract][Full Text] [Related]
3. Enhancing diabetic retinopathy classification using deep learning.
Alwakid G; Gouda W; Humayun M; Jhanjhi NZ
Digit Health; 2023; 9():20552076231203676. PubMed ID: 37766903
[TBL] [Abstract][Full Text] [Related]
4. Deep learning-enhanced diabetic retinopathy image classification.
Alwakid G; Gouda W; Humayun M; Jhanjhi NZ
Digit Health; 2023; 9():20552076231194942. PubMed ID: 37588156
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images.
Kobat SG; Baygin N; Yusufoglu E; Baygin M; Barua PD; Dogan S; Yaman O; Celiker U; Yildirim H; Tan RS; Tuncer T; Islam N; Acharya UR
Diagnostics (Basel); 2022 Aug; 12(8):. PubMed ID: 36010325
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. A Lightweight Diabetic Retinopathy Detection Model Using a Deep-Learning Technique.
Wahab Sait AR
Diagnostics (Basel); 2023 Oct; 13(19):. PubMed ID: 37835861
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy.
Mondal SS; Mandal N; Singh KK; Singh A; Izonin I
Diagnostics (Basel); 2022 Dec; 13(1):. PubMed ID: 36611416
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset.
Roy S; Tyagi M; Bansal V; Jain V
Comput Biol Med; 2022 Nov; 150():106092. PubMed ID: 36208598
[TBL] [Abstract][Full Text] [Related]
14. Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans.
Nneji GU; Cai J; Deng J; Monday HN; Hossin MA; Nahar S
Diagnostics (Basel); 2022 Feb; 12(2):. PubMed ID: 35204628
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection.
Khalifa NEM; Loey M; Taha MHN; Mohamed HNET
Acta Inform Med; 2019 Dec; 27(5):327-332. PubMed ID: 32210500
[TBL] [Abstract][Full Text] [Related]
17. Optimized hybrid machine learning approach for smartphone based diabetic retinopathy detection.
Gupta S; Thakur S; Gupta A
Multimed Tools Appl; 2022; 81(10):14475-14501. PubMed ID: 35233182
[TBL] [Abstract][Full Text] [Related]
18. Segmentation of retinal blood vessels by a novel hybrid technique- Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE).
Sidhu RK; Sachdeva J; Katoch D
Microvasc Res; 2023 Jul; 148():104477. PubMed ID: 36746364
[TBL] [Abstract][Full Text] [Related]
19. Automated detection of diabetic retinopathy using custom convolutional neural network.
Albahli S; Ahmad Hassan Yar GN
J Xray Sci Technol; 2022; 30(2):275-291. PubMed ID: 35001904
[TBL] [Abstract][Full Text] [Related]
20. A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.
Liu X; Dong X; Li T; Zou X; Cheng C; Jiang Z; Gao Z; Duan S; Chen M; Liu T; Huang P; Li D; Lu H
Quant Imaging Med Surg; 2024 Jan; 14(1):861-876. PubMed ID: 38223039
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
