158 related articles for article (PubMed ID: 37903967)
41. 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]
42. Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE Features.
Mohan NJ; Murugan R; Goel T; Roy P
J Digit Imaging; 2022 Jun; 35(3):496-513. PubMed ID: 35141807
[TBL] [Abstract][Full Text] [Related]
43. Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm.
Ren F; Cao P; Li W; Zhao D; Zaiane O
Comput Med Imaging Graph; 2017 Jan; 55():54-67. PubMed ID: 27507324
[TBL] [Abstract][Full Text] [Related]
44. Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network.
Deepa V; Sathish Kumar C; Cherian T
Phys Eng Sci Med; 2022 Jun; 45(2):623-635. PubMed ID: 35587313
[TBL] [Abstract][Full Text] [Related]
45. Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms.
Tsao HY; Chan PY; Su EC
BMC Bioinformatics; 2018 Aug; 19(Suppl 9):283. PubMed ID: 30367589
[TBL] [Abstract][Full Text] [Related]
46. Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images.
Eladawi N; Elmogy M; Khalifa F; Ghazal M; Ghazi N; Aboelfetouh A; Riad A; Sandhu H; Schaal S; El-Baz A
Med Phys; 2018 Oct; 45(10):4582-4599. PubMed ID: 30144102
[TBL] [Abstract][Full Text] [Related]
47. Detection of neovascularization in retinal images using multivariate m-Mediods based classifier.
Usman Akram M; Khalid S; Tariq A; Younus Javed M
Comput Med Imaging Graph; 2013; 37(5-6):346-57. PubMed ID: 23916066
[TBL] [Abstract][Full Text] [Related]
48. Prediction of diabetic protein markers based on an ensemble method.
Qu K; Zou Q; Shi H
Front Biosci (Landmark Ed); 2021 Jul; 26(7):207-221. PubMed ID: 34340268
[No Abstract] [Full Text] [Related]
49. Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods.
Taghizadeh E; Heydarheydari S; Saberi A; JafarpoorNesheli S; Rezaeijo SM
BMC Bioinformatics; 2022 Oct; 23(1):410. PubMed ID: 36183055
[TBL] [Abstract][Full Text] [Related]
50. An ensemble deep learning based approach for red lesion detection in fundus images.
Orlando JI; Prokofyeva E; Del Fresno M; Blaschko MB
Comput Methods Programs Biomed; 2018 Jan; 153():115-127. PubMed ID: 29157445
[TBL] [Abstract][Full Text] [Related]
51. DREAM: diabetic retinopathy analysis using machine learning.
Roychowdhury S; Koozekanani DD; Parhi KK
IEEE J Biomed Health Inform; 2014 Sep; 18(5):1717-28. PubMed ID: 25192577
[TBL] [Abstract][Full Text] [Related]
52. IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.
Porwal P; Pachade S; Kokare M; Deshmukh G; Son J; Bae W; Liu L; Wang J; Liu X; Gao L; Wu T; Xiao J; Wang F; Yin B; Wang Y; Danala G; He L; Choi YH; Lee YC; Jung SH; Li Z; Sui X; Wu J; Li X; Zhou T; Toth J; Baran A; Kori A; Chennamsetty SS; Safwan M; Alex V; Lyu X; Cheng L; Chu Q; Li P; Ji X; Zhang S; Shen Y; Dai L; Saha O; Sathish R; Melo T; Araújo T; Harangi B; Sheng B; Fang R; Sheet D; Hajdu A; Zheng Y; Mendonça AM; Zhang S; Campilho A; Zheng B; Shen D; Giancardo L; Quellec G; Mériaudeau F
Med Image Anal; 2020 Jan; 59():101561. PubMed ID: 31671320
[TBL] [Abstract][Full Text] [Related]
53. Segmentation of retinal blood vessels by a novel hybrid technique- Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE).
Sidhu RK; Sachdeva J; Katoch D
Microvasc Res; 2023 Jul; 148():104477. PubMed ID: 36746364
[TBL] [Abstract][Full Text] [Related]
54. Optical imaging for diabetic retinopathy diagnosis and detection using ensemble models.
Pavithra S; Jaladi D; Tamilarasi K
Photodiagnosis Photodyn Ther; 2024 Jun; 48():104259. PubMed ID: 38944405
[TBL] [Abstract][Full Text] [Related]
55. Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images.
Sopharak A; Uyyanonvara B; Barman S
Comput Med Imaging Graph; 2013; 37(5-6):394-402. PubMed ID: 23777979
[TBL] [Abstract][Full Text] [Related]
56. Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.
Al-Jarrah MA; Shatnawi H
J Med Eng Technol; 2017 Aug; 41(6):498-505. PubMed ID: 28786703
[TBL] [Abstract][Full Text] [Related]
57. Efficient multi-kernel multi-instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis.
Cao P; Ren F; Wan C; Yang J; Zaiane O
Comput Med Imaging Graph; 2018 Nov; 69():112-124. PubMed ID: 30237145
[TBL] [Abstract][Full Text] [Related]
58. Convolutional neural network-based sea lion optimization algorithm for the detection and classification of diabetic retinopathy.
Hemanth SV; Alagarsamy S; Dhiliphan Rajkumar T
Acta Diabetol; 2023 Oct; 60(10):1377-1389. PubMed ID: 37368025
[TBL] [Abstract][Full Text] [Related]
59. Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.
Li F; Zhao C; Xia Z; Wang Y; Zhou X; Li GZ
BMC Complement Altern Med; 2012 Aug; 12():127. PubMed ID: 22898352
[TBL] [Abstract][Full Text] [Related]
60. Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images.
Sharafeldeen A; Elsharkawy M; Khalifa F; Soliman A; Ghazal M; AlHalabi M; Yaghi M; Alrahmawy M; Elmougy S; Sandhu HS; El-Baz A
Sci Rep; 2021 Feb; 11(1):4730. PubMed ID: 33633139
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]