164 related articles for article (PubMed ID: 33350559)
1. Classification of retinal images based on convolutional neural network.
El-Hag NA; Sedik A; El-Shafai W; El-Hoseny HM; Khalaf AAM; El-Fishawy AS; Al-Nuaimy W; Abd El-Samie FE; El-Banby GM
Microsc Res Tech; 2021 Mar; 84(3):394-414. PubMed ID: 33350559
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images.
Medhi JP; Dandapat S
Comput Biol Med; 2016 Jul; 74():30-44. PubMed ID: 27174686
[TBL] [Abstract][Full Text] [Related]
4. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
Liu Q; Zou B; Chen J; Ke W; Yue K; Chen Z; Zhao G
Comput Med Imaging Graph; 2017 Jan; 55():78-86. PubMed ID: 27665058
[TBL] [Abstract][Full Text] [Related]
5. Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy.
Khojasteh P; Aliahmad B; Arjunan SP; Kumar DK
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5938-5941. PubMed ID: 30441688
[TBL] [Abstract][Full Text] [Related]
6. Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.
Zhong P; Wang J; Guo Y; Fu X; Wang R
Appl Opt; 2020 Nov; 59(33):10312-10320. PubMed ID: 33361962
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation.
Reza AW; Eswaran C; Dimyati K
J Med Syst; 2011 Dec; 35(6):1491-501. PubMed ID: 20703768
[TBL] [Abstract][Full Text] [Related]
9. Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks.
Sule O; Viriri S
J Digit Imaging; 2023 Apr; 36(2):414-432. PubMed ID: 36456839
[TBL] [Abstract][Full Text] [Related]
10. Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation.
Long S; Huang X; Chen Z; Pardhan S; Zheng D
Biomed Res Int; 2019; 2019():3926930. PubMed ID: 30809539
[TBL] [Abstract][Full Text] [Related]
11. Artificial Humming Bird Optimization-Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images.
E D; S SP; R P; C BS
J Digit Imaging; 2023 Feb; 36(1):59-72. PubMed ID: 36241944
[TBL] [Abstract][Full Text] [Related]
12. A novel method for retinal optic disc detection using bat meta-heuristic algorithm.
Abdullah AS; Özok YE; Rahebi J
Med Biol Eng Comput; 2018 Nov; 56(11):2015-2024. PubMed ID: 29740745
[TBL] [Abstract][Full Text] [Related]
13. Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.
Fang L; Wang C; Li S; Rabbani H; Chen X; Liu Z
IEEE Trans Med Imaging; 2019 Aug; 38(8):1959-1970. PubMed ID: 30763240
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Fractal-based automatic localization and segmentation of optic disc in retinal images.
Ying H; Zhang M; Liu JC
Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():4139-41. PubMed ID: 18002913
[TBL] [Abstract][Full Text] [Related]
16. Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions.
Al-Mukhtar M; Morad AH; Albadri M; Islam MDS
Sci Rep; 2021 Dec; 11(1):23631. PubMed ID: 34880311
[TBL] [Abstract][Full Text] [Related]
17. Analysis on diagnosing diabetic retinopathy by segmenting blood vessels, optic disc and retinal abnormalities.
Jadhav AS; Patil PB; Biradar S
J Med Eng Technol; 2020 Aug; 44(6):299-316. PubMed ID: 32729345
[TBL] [Abstract][Full Text] [Related]
18. [Automatic detection of exudates in retinal images based on threshold moving average models].
Wisaeng K; Hiransakolwong N; Pothiruk E
Biofizika; 2015; 60(2):360-70. PubMed ID: 26016034
[TBL] [Abstract][Full Text] [Related]
19. A novel retinal vessel detection approach based on multiple deep convolution neural networks.
Guo Y; Budak Ü; Şengür A
Comput Methods Programs Biomed; 2018 Dec; 167():43-48. PubMed ID: 30501859
[TBL] [Abstract][Full Text] [Related]
20. Semantic segmentation of retinal exudates using a residual encoder-decoder architecture in diabetic retinopathy.
Manan MA; Jinchao F; Khan TM; Yaqub M; Ahmed S; Chuhan IS
Microsc Res Tech; 2023 Nov; 86(11):1443-1460. PubMed ID: 37194727
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]