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 *

138 related articles for article (PubMed ID: 37420891)

  • 41. Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.
    Pan X; Jin K; Cao J; Liu Z; Wu J; You K; Lu Y; Xu Y; Su Z; Jiang J; Yao K; Ye J
    Graefes Arch Clin Exp Ophthalmol; 2020 Apr; 258(4):779-785. PubMed ID: 31932886
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Micro-segmentation of retinal image lesions in diabetic retinopathy using energy-based fuzzy C-Means clustering (EFM-FCM).
    Naz H; Nijhawan R; Ahuja NJ; Saba T; Alamri FS; Rehman A
    Microsc Res Tech; 2024 Jan; 87(1):78-94. PubMed ID: 37681440
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model.
    Bhardwaj C; Jain S; Sood M
    J Digit Imaging; 2021 Apr; 34(2):440-457. PubMed ID: 33686525
    [TBL] [Abstract][Full Text] [Related]  

  • 44. DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.
    Araújo T; Aresta G; Mendonça L; Penas S; Maia C; Carneiro Â; Mendonça AM; Campilho A
    Med Image Anal; 2020 Jul; 63():101715. PubMed ID: 32434128
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Diabetic retinopathy prediction based on vision transformer and modified capsule network.
    Oulhadj M; Riffi J; Khodriss C; Mahraz AM; Yahyaouy A; Abdellaoui M; Andaloussi IB; Tairi H
    Comput Biol Med; 2024 Jun; 175():108523. PubMed ID: 38701591
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Recognition of Diabetic Retinopathy with Ground Truth Segmentation Using Fundus Images and Neural Network Algorithm.
    Kshirsagar PR; Manoharan H; Meshram P; Alqahtani J; Naveed QN; Islam S; Abebe TG
    Comput Intell Neurosci; 2022; 2022():8356081. PubMed ID: 36211022
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Recognition of diabetic retinopathy and macular edema using deep learning.
    Jeribi F; Nazir T; Nawaz M; Javed A; Alhameed M; Tahir A
    Med Biol Eng Comput; 2024 Sep; 62(9):2687-2701. PubMed ID: 38684593
    [TBL] [Abstract][Full Text] [Related]  

  • 48. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.
    Ryu G; Lee K; Park D; Park SH; Sagong M
    Sci Rep; 2021 Nov; 11(1):23024. PubMed ID: 34837030
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.
    Khan A; Pin K; Aziz A; Han JW; Nam Y
    Sensors (Basel); 2023 Jul; 23(15):. PubMed ID: 37571490
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Comparison review of image classification techniques for early diagnosis of diabetic retinopathy.
    Wangweera C; Zanini P
    Biomed Phys Eng Express; 2024 Sep; 10(6):. PubMed ID: 39173657
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.
    Shamsan A; Senan EM; Ahmad Shatnawi HS
    PLoS One; 2023; 18(10):e0289555. PubMed ID: 37862328
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography.
    Heisler M; Karst S; Lo J; Mammo Z; Yu T; Warner S; Maberley D; Beg MF; Navajas EV; Sarunic MV
    Transl Vis Sci Technol; 2020 Apr; 9(2):20. PubMed ID: 32818081
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy.
    Tariq H; Rashid M; Javed A; Zafar E; Alotaibi SS; Zia MYI
    Sensors (Basel); 2021 Dec; 22(1):. PubMed ID: 35009747
    [TBL] [Abstract][Full Text] [Related]  

  • 54. An adaptive weighted ensemble learning network for diabetic retinopathy classification.
    Wu P; Qu Y; Zhao Z; Cui Y; Xu Y; An P; Yu H
    J Xray Sci Technol; 2024; 32(2):285-301. PubMed ID: 38217630
    [TBL] [Abstract][Full Text] [Related]  

  • 55. 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]  

  • 56. Effective methods of diabetic retinopathy detection based on deep convolutional neural networks.
    Gu Y; Wang X; Pan J; Yong Z; Guo S; Pan T; Jiao Y; Zhou Z
    Int J Comput Assist Radiol Surg; 2021 Dec; 16(12):2177-2187. PubMed ID: 34606059
    [TBL] [Abstract][Full Text] [Related]  

  • 57. 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]  

  • 58. Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning.
    Atcı ŞY; Güneş A; Zontul M; Arslan Z
    Tomography; 2024 Feb; 10(2):215-230. PubMed ID: 38393285
    [TBL] [Abstract][Full Text] [Related]  

  • 59. 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]  

  • 60. An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.
    Singh S; Banoub R; Sanghvi HA; Agarwal A; Chalam KV; Gupta S; Pandya AS
    Curr Med Imaging; 2024; 20():e15734056286918. PubMed ID: 38721793
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.