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 *

223 related articles for article (PubMed ID: 30448367)

  • 1. Diabetic retinopathy techniques in retinal images: A review.
    Salamat N; Missen MMS; Rashid A
    Artif Intell Med; 2019 Jun; 97():168-188. PubMed ID: 30448367
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.
    Akyol K; Şen B; Bayır Ş
    Comput Math Methods Med; 2016; 2016():6814791. PubMed ID: 27110272
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A review on computer-aided recent developments for automatic detection of diabetic retinopathy.
    Randive SN; Senapati RK; Rahulkar AD
    J Med Eng Technol; 2019 Feb; 43(2):87-99. PubMed ID: 31198073
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.
    Asiri N; Hussain M; Al Adel F; Alzaidi N
    Artif Intell Med; 2019 Aug; 99():101701. PubMed ID: 31606116
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.
    Seoud L; Hurtut T; Chelbi J; Cheriet F; Langlois JM
    IEEE Trans Med Imaging; 2016 Apr; 35(4):1116-26. PubMed ID: 26701180
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection.
    AbdelMaksoud E; Barakat S; Elmogy M
    Comput Biol Med; 2020 Nov; 126():104039. PubMed ID: 33068807
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis.
    Gegundez-Arias ME; Marin D; Ponte B; Alvarez F; Garrido J; Ortega C; Vasallo MJ; Bravo JM
    Comput Biol Med; 2017 Sep; 88():100-109. PubMed ID: 28711766
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. [Retinal image analysis to detect lesions associated with diabetic retinopathy].
    Sánchez Gutiérrez CI; López Gálvez MI; Hornero Sánchez R; Poza Crespo J
    Arch Soc Esp Oftalmol; 2004 Dec; 79(12):623-8. PubMed ID: 15627932
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.
    Xiao Z; Zhang X; Geng L; Zhang F; Wu J; Tong J; Ogunbona PO; Shan C
    Biomed Eng Online; 2017 Oct; 16(1):122. PubMed ID: 29073912
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Distinguising Proof of Diabetic Retinopathy Detection by Hybrid Approaches in Two Dimensional Retinal Fundus Images.
    S K; D M
    J Med Syst; 2019 May; 43(6):173. PubMed ID: 31069550
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).
    Noor-Ul-Huda M; Tehsin S; Ahmed S; Niazi FAK; Murtaza Z
    Biomed Tech (Berl); 2019 May; 64(3):297-307. PubMed ID: 30055096
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Secondary Observer System for Detection of Microaneurysms in Fundus Images Using Texture Descriptors.
    Derwin DJ; Selvi ST; Singh OJ
    J Digit Imaging; 2020 Feb; 33(1):159-167. PubMed ID: 31144148
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier.
    Pratheeba C; Singh NN
    J Med Syst; 2019 May; 43(7):180. PubMed ID: 31093787
    [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. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review.
    Faust O; Acharya U R; Ng EY; Ng KH; Suri JS
    J Med Syst; 2012 Feb; 36(1):145-57. PubMed ID: 20703740
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy.
    Kar SS; Maity SP
    IEEE Trans Biomed Eng; 2018 Mar; 65(3):608-618. PubMed ID: 28541892
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.
    Raja DS; Vasuki S
    Comput Math Methods Med; 2015; 2015():419279. PubMed ID: 25810749
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.