BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 36707608)

  • 1. Deep learning-based hemorrhage detection for diabetic retinopathy screening.
    Aziz T; Charoenlarpnopparut C; Mahapakulchai S
    Sci Rep; 2023 Jan; 13(1):1479. PubMed ID: 36707608
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
    Gulshan V; Peng L; Coram M; Stumpe MC; Wu D; Narayanaswamy A; Venugopalan S; Widner K; Madams T; Cuadros J; Kim R; Raman R; Nelson PC; Mega JL; Webster DR
    JAMA; 2016 Dec; 316(22):2402-2410. PubMed ID: 27898976
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages.
    Aziz T; Charoenlarpnopparut C; Mahapakulchai S
    J Healthc Eng; 2022; 2022():7387174. PubMed ID: 36444209
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Deep feed forward neural network-based screening system for diabetic retinopathy severity classification using the lion optimization algorithm.
    Vasireddi HK; K SD; G N V RR
    Graefes Arch Clin Exp Ophthalmol; 2022 Apr; 260(4):1245-1263. PubMed ID: 34505925
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.
    Hua CH; Huynh-The T; Kim K; Yu SY; Le-Tien T; Park GH; Bang J; Khan WA; Bae SH; Lee S
    Int J Med Inform; 2019 Dec; 132():103926. PubMed ID: 31605882
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
    Tsiknakis N; Theodoropoulos D; Manikis G; Ktistakis E; Boutsora O; Berto A; Scarpa F; Scarpa A; Fotiadis DI; Marias K
    Comput Biol Med; 2021 Aug; 135():104599. PubMed ID: 34247130
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.
    Yang K; Lu Y; Xue L; Yang Y; Chang S; Zhou C
    Exp Biol Med (Maywood); 2023 Jun; 248(11):909-921. PubMed ID: 37466156
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.
    Son J; Shin JY; Kim HD; Jung KH; Park KH; Park SJ
    Ophthalmology; 2020 Jan; 127(1):85-94. PubMed ID: 31281057
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An Efficient Deep Learning Network for Automatic Detection of Neovascularization in Color Fundus Images.
    Huang H; Wang X; Ma H
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3688-3692. PubMed ID: 34892037
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Vision Transformer-based recognition of diabetic retinopathy grade.
    Wu J; Hu R; Xiao Z; Chen J; Liu J
    Med Phys; 2021 Dec; 48(12):7850-7863. PubMed ID: 34693536
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 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 9.