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 *

141 related articles for article (PubMed ID: 37892058)

  • 1. HDR-EfficientNet: A Classification of Hypertensive and Diabetic Retinopathy Using Optimize EfficientNet Architecture.
    Abbas Q; Daadaa Y; Rashid U; Sajid MZ; Ibrahim MEA
    Diagnostics (Basel); 2023 Oct; 13(20):. PubMed ID: 37892058
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet.
    Vijayan M; S V
    Diagnostics (Basel); 2023 Feb; 13(4):. PubMed ID: 36832262
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. A hybrid neural network approach for classifying diabetic retinopathy subtypes.
    Xu H; Shao X; Fang D; Huang F
    Front Med (Lausanne); 2023; 10():1293019. PubMed ID: 38239623
    [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. 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]  

  • 7. Explainable Diabetic Retinopathy using EfficientNET
    Chetoui M; Akhloufi MA
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1966-1969. PubMed ID: 33018388
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture.
    Sajid MZ; Qureshi I; Abbas Q; Albathan M; Shaheed K; Youssef A; Ferdous S; Hussain A
    Diagnostics (Basel); 2023 Apr; 13(8):. PubMed ID: 37189539
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases.
    Abbas Q; Albathan M; Altameem A; Almakki RS; Hussain A
    Diagnostics (Basel); 2023 Oct; 13(20):. PubMed ID: 37891986
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture.
    Abbas Q; Qureshi I; Ibrahim MEA
    Sensors (Basel); 2021 Oct; 21(20):. PubMed ID: 34696149
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.
    Farahat Z; Zrira N; Souissi N; Bennani Y; Bencherif S; Benamar S; Belmekki M; Ngote MN; Megdiche K
    Surv Ophthalmol; 2024; 69(5):707-721. PubMed ID: 38885761
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Data Diversity in Convolutional Neural Network Based Ensemble Model for Diabetic Retinopathy.
    Inamullah ; Hassan S; Alrajeh NA; Mohammed EA; Khan S
    Biomimetics (Basel); 2023 Apr; 8(2):. PubMed ID: 37218773
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study.
    Han Y; Li W; Liu M; Wu Z; Zhang F; Liu X; Tao L; Li X; Guo X
    J Med Internet Res; 2021 Jul; 23(7):e27822. PubMed ID: 34255681
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets.
    Chetoui M; Akhloufi MA
    J Med Imaging (Bellingham); 2020 Jul; 7(4):044503. PubMed ID: 32904519
    [No Abstract]   [Full Text] [Related]  

  • 18. Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm.
    Li F; Liu Z; Chen H; Jiang M; Zhang X; Wu Z
    Transl Vis Sci Technol; 2019 Nov; 8(6):4. PubMed ID: 31737428
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.
    Li Q; Zhu XR; Sun G; Zhang L; Zhu M; Tian T; Guo C; Mazhar S; Yang JK; Li Y
    Comput Math Methods Med; 2022; 2022():4316507. PubMed ID: 35966243
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.