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 *

197 related articles for article (PubMed ID: 27777811)

  • 1. A Review on Recent Developments for Detection of Diabetic Retinopathy.
    Amin J; Sharif M; Yasmin M
    Scientifica (Cairo); 2016; 2016():6838976. PubMed ID: 27777811
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Computer-aided diagnosis of diabetic retinopathy: a review.
    Mookiah MR; Acharya UR; Chua CK; Lim CM; Ng EY; Laude A
    Comput Biol Med; 2013 Dec; 43(12):2136-55. PubMed ID: 24290931
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Detection of retinal lesions in diabetic retinopathy: comparative evaluation of 7-field digital color photography versus red-free photography.
    Venkatesh P; Sharma R; Vashist N; Vohra R; Garg S
    Int Ophthalmol; 2015 Oct; 35(5):635-40. PubMed ID: 22961609
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Survey on recent developments in automatic detection of diabetic retinopathy.
    Bilal A; Sun G; Mazhar S
    J Fr Ophtalmol; 2021 Mar; 44(3):420-440. PubMed ID: 33526268
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. Diabetic retinopathy grading by digital curvelet transform.
    Hajeb Mohammad Alipour S; Rabbani H; Akhlaghi MR
    Comput Math Methods Med; 2012; 2012():761901. PubMed ID: 23056148
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features.
    Mahmood MAI; Aktar N; Kader MF
    Heliyon; 2023 Sep; 9(9):e19625. PubMed ID: 37809795
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review.
    Dubey S; Dixit M
    Multimed Tools Appl; 2023; 82(10):14471-14525. PubMed ID: 36185322
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Computer-based detection of diabetes retinopathy stages using digital fundus images.
    Acharya UR; Lim CM; Ng EY; Chee C; Tamura T
    Proc Inst Mech Eng H; 2009 Jul; 223(5):545-53. PubMed ID: 19623908
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study.
    Wu H; Zhang X; Geng X; Dong J; Zhou G
    BMC Ophthalmol; 2014 Oct; 14():126. PubMed ID: 25359611
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels.
    Srivastava R; Duan L; Wong DWK; Liu J; Wong TY
    Comput Methods Programs Biomed; 2017 Jan; 138():83-91. PubMed ID: 27886718
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A deep learning system for detecting diabetic retinopathy across the disease spectrum.
    Dai L; Wu L; Li H; Cai C; Wu Q; Kong H; Liu R; Wang X; Hou X; Liu Y; Long X; Wen Y; Lu L; Shen Y; Chen Y; Shen D; Yang X; Zou H; Sheng B; Jia W
    Nat Commun; 2021 May; 12(1):3242. PubMed ID: 34050158
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.
    Khojasteh P; Aliahmad B; Kumar DK
    BMC Ophthalmol; 2018 Nov; 18(1):288. PubMed ID: 30400869
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A novel method for retinal exudate segmentation using signal separation algorithm.
    Imani E; Pourreza HR
    Comput Methods Programs Biomed; 2016 Sep; 133():195-205. PubMed ID: 27393810
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.