195 related articles for article (PubMed ID: 35233182)
1. 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]
2. Analysis on diagnosing diabetic retinopathy by segmenting blood vessels, optic disc and retinal abnormalities.
Jadhav AS; Patil PB; Biradar S
J Med Eng Technol; 2020 Aug; 44(6):299-316. PubMed ID: 32729345
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. Deep Learning Frameworks for Diabetic Retinopathy Detection with Smartphone-based Retinal Imaging Systems.
Hacisoftaoglu RE; Karakaya M; Sallam AB
Pattern Recognit Lett; 2020 Jul; 135():409-417. PubMed ID: 32704196
[TBL] [Abstract][Full Text] [Related]
7. A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features.
Mahmood MAI; Aktar N; Kader MF
Heliyon; 2023 Sep; 9(9):e19625. PubMed ID: 37809795
[TBL] [Abstract][Full Text] [Related]
8. Deep long and short term memory based Red Fox optimization algorithm for diabetic retinopathy detection and classification.
Pugal Priya R; Saradadevi Sivarani T; Gnana Saravanan A
Int J Numer Method Biomed Eng; 2022 Mar; 38(3):e3560. PubMed ID: 34865312
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. A novel deep learning model for diabetic retinopathy detection in retinal fundus images using pre-trained CNN and HWBLSTM.
Hemanth SV; Alagarsamy S; Rajkumar TD
J Biomol Struct Dyn; 2024 Feb; ():1-19. PubMed ID: 38373067
[TBL] [Abstract][Full Text] [Related]
12. A Hybrid Technique for Diabetic Retinopathy Detection Based on Ensemble-Optimized CNN and Texture Features.
Ishtiaq U; Abdullah ERMF; Ishtiaque Z
Diagnostics (Basel); 2023 May; 13(10):. PubMed ID: 37238304
[TBL] [Abstract][Full Text] [Related]
13. Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features.
Butt MM; Iskandar DNFA; Abdelhamid SE; Latif G; Alghazo R
Diagnostics (Basel); 2022 Jul; 12(7):. PubMed ID: 35885512
[TBL] [Abstract][Full Text] [Related]
14. Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy.
Berbar MA
Health Inf Sci Syst; 2022 Dec; 10(1):14. PubMed ID: 35782197
[TBL] [Abstract][Full Text] [Related]
15. 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]
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. 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]
18. Convolutional neural network-based sea lion optimization algorithm for the detection and classification of diabetic retinopathy.
Hemanth SV; Alagarsamy S; Dhiliphan Rajkumar T
Acta Diabetol; 2023 Oct; 60(10):1377-1389. PubMed ID: 37368025
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
