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 *

207 related articles for article (PubMed ID: 37420891)

  • 1. 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]  

  • 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. 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]  

  • 4. 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]  

  • 5. A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique.
    AbdelMaksoud E; Barakat S; Elmogy M
    Med Biol Eng Comput; 2022 Jul; 60(7):2015-2038. PubMed ID: 35545738
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Optical imaging for diabetic retinopathy diagnosis and detection using ensemble models.
    Pavithra S; Jaladi D; Tamilarasi K
    Photodiagnosis Photodyn Ther; 2024 Aug; 48():104259. PubMed ID: 38944405
    [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. Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.
    Farooq MS; Arooj A; Alroobaea R; Baqasah AM; Jabarulla MY; Singh D; Sardar R
    Sensors (Basel); 2022 Feb; 22(5):. PubMed ID: 35270949
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Hybrid machine learning architecture for automated detection and grading of retinal images for diabetic retinopathy.
    Narayanan BN; Hardie RC; De Silva MS; Kueterman NK
    J Med Imaging (Bellingham); 2020 May; 7(3):034501. PubMed ID: 32613029
    [No Abstract]   [Full Text] [Related]  

  • 11. 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]  

  • 12. Transfer learning-driven ensemble model for detection of diabetic retinopathy disease.
    Chaurasia BK; Raj H; Rathour SS; Singh PB
    Med Biol Eng Comput; 2023 Aug; 61(8):2033-2049. PubMed ID: 37296285
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity.
    Vives-Boix V; Ruiz-Fernández D
    Comput Methods Programs Biomed; 2021 Jul; 206():106094. PubMed ID: 34010801
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.
    Gao Z; Jin K; Yan Y; Liu X; Shi Y; Ge Y; Pan X; Lu Y; Wu J; Wang Y; Ye J
    Graefes Arch Clin Exp Ophthalmol; 2022 May; 260(5):1663-1673. PubMed ID: 35066704
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets.
    Saini M; Susan S
    Comput Biol Med; 2022 Oct; 149():105989. PubMed ID: 36037631
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An automated unsupervised deep learning-based approach for diabetic retinopathy detection.
    Naz H; Nijhawan R; Ahuja NJ
    Med Biol Eng Comput; 2022 Dec; 60(12):3635-3654. PubMed ID: 36274090
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 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 new ultra-wide-field fundus dataset to diabetic retinopathy grading using hybrid preprocessing methods.
    Liu H; Teng L; Fan L; Sun Y; Li H
    Comput Biol Med; 2023 May; 157():106750. PubMed ID: 36931202
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.