158 related articles for article (PubMed ID: 37903967)
1. A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach.
Uppamma P; Bhattacharya S
Sci Rep; 2023 Oct; 13(1):18572. PubMed ID: 37903967
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection.
Ullah H; Saba T; Islam N; Abbas N; Rehman A; Mehmood Z; Anjum A
Microsc Res Tech; 2019 Apr; 82(4):361-372. PubMed ID: 30677193
[TBL] [Abstract][Full Text] [Related]
4. An adaptive weighted ensemble learning network for diabetic retinopathy classification.
Wu P; Qu Y; Zhao Z; Cui Y; Xu Y; An P; Yu H
J Xray Sci Technol; 2024; 32(2):285-301. PubMed ID: 38217630
[TBL] [Abstract][Full Text] [Related]
5. Distinguising Proof of Diabetic Retinopathy Detection by Hybrid Approaches in Two Dimensional Retinal Fundus Images.
S K; D M
J Med Syst; 2019 May; 43(6):173. PubMed ID: 31069550
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Automated classification of diabetic retinopathy through reliable feature selection.
Gayathri S; Gopi VP; Palanisamy P
Phys Eng Sci Med; 2020 Sep; 43(3):927-945. PubMed ID: 32648111
[TBL] [Abstract][Full Text] [Related]
9. A novel four-step feature selection technique for diabetic retinopathy grading.
Jagan Mohan N; Murugan R; Goel T; Mirjalili S; Roy P
Phys Eng Sci Med; 2021 Dec; 44(4):1351-1366. PubMed ID: 34748191
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Hard exudates segmentation based on learned initial seeds and iterative graph cut.
Kusakunniran W; Wu Q; Ritthipravat P; Zhang J
Comput Methods Programs Biomed; 2018 May; 158():173-183. PubMed ID: 29544783
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection.
AbdelMaksoud E; Barakat S; Elmogy M
Comput Biol Med; 2020 Nov; 126():104039. PubMed ID: 33068807
[TBL] [Abstract][Full Text] [Related]
14. Machine Learning Based Automatic Neovascularization Detection on Optic Disc Region.
Yu S; Xiao D; Kanagasingam Y
IEEE J Biomed Health Inform; 2018 May; 22(3):886-894. PubMed ID: 29727291
[TBL] [Abstract][Full Text] [Related]
15. A review on computer-aided recent developments for automatic detection of diabetic retinopathy.
Randive SN; Senapati RK; Rahulkar AD
J Med Eng Technol; 2019 Feb; 43(2):87-99. PubMed ID: 31198073
[TBL] [Abstract][Full Text] [Related]
16. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.
ElTanboly A; Ismail M; Shalaby A; Switala A; El-Baz A; Schaal S; Gimel'farb G; El-Azab M
Med Phys; 2017 Mar; 44(3):914-923. PubMed ID: 28035657
[TBL] [Abstract][Full Text] [Related]
17. Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait.
Haque F; Reaz MBI; Chowdhury MEH; Ezeddin M; Kiranyaz S; Alhatou M; Ali SHM; Bakar AAA; Srivastava G
Sensors (Basel); 2022 May; 22(9):. PubMed ID: 35591196
[TBL] [Abstract][Full Text] [Related]
18. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images.
Köse C; Sevik U; Ikibaş C; Erdöl H
Comput Methods Programs Biomed; 2012 Aug; 107(2):274-93. PubMed ID: 21757250
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning.
Ren F; Cao P; Zhao D; Wan C
Technol Health Care; 2018; 26(S1):389-397. PubMed ID: 29689762
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
