These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

605 related articles for article (PubMed ID: 34033015)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.
    Papadopoulos A; Topouzis F; Delopoulos A
    Sci Rep; 2021 Jul; 11(1):14326. PubMed ID: 34253799
    [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. Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network.
    Deepa V; Sathish Kumar C; Cherian T
    Phys Eng Sci Med; 2022 Jun; 45(2):623-635. PubMed ID: 35587313
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.
    Wu Z; Shi G; Chen Y; Shi F; Chen X; Coatrieux G; Yang J; Luo L; Li S
    Artif Intell Med; 2020 Aug; 108():101936. PubMed ID: 32972665
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.
    Liu YP; Li Z; Xu C; Li J; Liang R
    Artif Intell Med; 2019 Aug; 99():101694. PubMed ID: 31606108
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients.
    Maqsood S; Damaševičius R; Maskeliūnas R
    Sensors (Basel); 2021 Jun; 21(11):. PubMed ID: 34205120
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Non-uniform Label Smoothing for Diabetic Retinopathy Grading from Retinal Fundus Images with Deep Neural Networks.
    Galdran A; Chelbi J; Kobi R; Dolz J; Lombaert H; Ben Ayed I; Chakor H
    Transl Vis Sci Technol; 2020 Jun; 9(2):34. PubMed ID: 32832207
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. A deep learning framework for the early detection of multi-retinal diseases.
    Ejaz S; Baig R; Ashraf Z; Alnfiai MM; Alnahari MM; Alotaibi RM
    PLoS One; 2024; 19(7):e0307317. PubMed ID: 39052616
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 31.