164 related articles for article (PubMed ID: 38653842)
21. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications.
Prahs P; Radeck V; Mayer C; Cvetkov Y; Cvetkova N; Helbig H; Märker D
Graefes Arch Clin Exp Ophthalmol; 2018 Jan; 256(1):91-98. PubMed ID: 29127485
[TBL] [Abstract][Full Text] [Related]
22. Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.
Alsaih K; Lemaitre G; Rastgoo M; Massich J; Sidibé D; Meriaudeau F
Biomed Eng Online; 2017 Jun; 16(1):68. PubMed ID: 28592309
[TBL] [Abstract][Full Text] [Related]
23. Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities.
Hassan B; Hassan T; Li B; Ahmed R; Hassan O
Sensors (Basel); 2019 Jul; 19(13):. PubMed ID: 31284442
[TBL] [Abstract][Full Text] [Related]
24. Epiretinal Membrane Detection at the Ophthalmologist Level using Deep Learning of Optical Coherence Tomography.
Lo YC; Lin KH; Bair H; Sheu WH; Chang CS; Shen YC; Hung CL
Sci Rep; 2020 May; 10(1):8424. PubMed ID: 32439844
[TBL] [Abstract][Full Text] [Related]
25. DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.
Wu Q; Zhang B; Hu Y; Liu B; Cao D; Yang D; Peng Q; Zhong P; Zeng X; Xiao Y; Li C; Fang Y; Feng S; Huang M; Cai H; Yang X; Yu H
Retina; 2021 May; 41(5):1110-1117. PubMed ID: 33031250
[TBL] [Abstract][Full Text] [Related]
26. RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images.
Prabha AJ; Venkatesan C; Fathimal MS; Nithiyanantham KK; Kirubha SPA
Biomed Phys Eng Express; 2024 Feb; 10(2):. PubMed ID: 38335542
[TBL] [Abstract][Full Text] [Related]
27. Comparison of the proposed DCNN model with standard CNN architectures for retinal diseases classification.
Mohan R; Ganapathy K; Arunmozhi R
J Popul Ther Clin Pharmacol; 2022; 29(3):e112-e122. PubMed ID: 36196946
[TBL] [Abstract][Full Text] [Related]
28. Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning.
Saidi L; Jomaa H; Zainab H; Zgolli H; Mabrouk S; Sidibé D; Tabia H; Khlifa N
Int J Biomed Imaging; 2023; 2023():9966107. PubMed ID: 38046618
[TBL] [Abstract][Full Text] [Related]
29. Wavelet scattering transform application in classification of retinal abnormalities using OCT images.
Baharlouei Z; Rabbani H; Plonka G
Sci Rep; 2023 Nov; 13(1):19013. PubMed ID: 37923770
[TBL] [Abstract][Full Text] [Related]
30. Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study.
Akça S; Garip Z; Ekinci E; Atban F
Lasers Med Sci; 2024 May; 39(1):140. PubMed ID: 38797751
[TBL] [Abstract][Full Text] [Related]
31. Explainable ensemble learning method for OCT detection with transfer learning.
Yang J; Wang G; Xiao X; Bao M; Tian G
PLoS One; 2024; 19(3):e0296175. PubMed ID: 38517913
[TBL] [Abstract][Full Text] [Related]
32. AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.
Alqudah AM
Med Biol Eng Comput; 2020 Jan; 58(1):41-53. PubMed ID: 31728935
[TBL] [Abstract][Full Text] [Related]
33. Automatic Classification of Macular Diseases from OCT Images Using CNN Guided with Edge Convolutional Layer.
Esfahani EN; Daneshmand PG; Rabbani H; Plonka G
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():3858-3861. PubMed ID: 36085830
[TBL] [Abstract][Full Text] [Related]
34. Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.
Manikandan S; Raman R; Rajalakshmi R; Tamilselvi S; Surya RJ
Indian J Ophthalmol; 2023 May; 71(5):1783-1796. PubMed ID: 37203031
[TBL] [Abstract][Full Text] [Related]
35. Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.
Yellapragada B; Hornauer S; Snyder K; Yu S; Yiu G
Ophthalmol Retina; 2022 Feb; 6(2):116-129. PubMed ID: 34217854
[TBL] [Abstract][Full Text] [Related]
36. Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.
Lin M; Bao G; Sang X; Wu Y
Sensors (Basel); 2022 Apr; 22(8):. PubMed ID: 35459040
[TBL] [Abstract][Full Text] [Related]
37. Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.
Padilla-Pantoja FD; Sanchez YD; Quijano-Nieto BA; Perdomo OJ; Gonzalez FA
Transl Vis Sci Technol; 2022 Sep; 11(9):29. PubMed ID: 36169966
[TBL] [Abstract][Full Text] [Related]
38. En Face Doppler Optical Coherence Tomography Measurement of Total Retinal Blood Flow in Diabetic Retinopathy and Diabetic Macular Edema.
Lee B; Novais EA; Waheed NK; Adhi M; de Carlo TE; Cole ED; Moult EM; Choi W; Lane M; Baumal CR; Duker JS; Fujimoto JG
JAMA Ophthalmol; 2017 Mar; 135(3):244-251. PubMed ID: 28196198
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.
Rasti R; Mehridehnavi A; Rabbani H; Hajizadeh F
J Biomed Opt; 2018 Mar; 23(3):1-10. PubMed ID: 29564864
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
