BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

133 related articles for article (PubMed ID: 34891997)

  • 1. The Influence of Age and Gender Information on the Diagnosis of Diabetic Retinopathy: Based on Neural Networks.
    Bai L; Chen S; Gao M; Abdelrahman L; Ghamdi MA; Abdel-Mottaleb M
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3514-3517. PubMed ID: 34891997
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.
    Xu K; Feng D; Mi H
    Molecules; 2017 Nov; 22(12):. PubMed ID: 29168750
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity.
    Vives-Boix V; Ruiz-Fernández D
    Comput Methods Programs Biomed; 2021 Jul; 206():106094. PubMed ID: 34010801
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features.
    Harangi B; Toth J; Baran A; Hajdu A
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():2699-2702. PubMed ID: 31946452
    [TBL] [Abstract][Full Text] [Related]  

  • 6. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.
    Gao Z; Jin K; Yan Y; Liu X; Shi Y; Ge Y; Pan X; Lu Y; Wu J; Wang Y; Ye J
    Graefes Arch Clin Exp Ophthalmol; 2022 May; 260(5):1663-1673. PubMed ID: 35066704
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A novel four-step feature selection technique for diabetic retinopathy grading.
    Jagan Mohan N; Murugan R; Goel T; Mirjalili S; Roy P
    Phys Eng Sci Med; 2021 Dec; 44(4):1351-1366. PubMed ID: 34748191
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.
    Liu YP; Li Z; Xu C; Li J; Liang R
    Artif Intell Med; 2019 Aug; 99():101694. PubMed ID: 31606108
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Detection of Fundus Lesions through a Convolutional Neural Network in Patients with Diabetic Retinopathy.
    Santos C; de Aguiar MS; Welfer D; Belloni BM
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():2692-2695. PubMed ID: 34891806
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.
    Wu Z; Shi G; Chen Y; Shi F; Chen X; Coatrieux G; Yang J; Luo L; Li S
    Artif Intell Med; 2020 Aug; 108():101936. PubMed ID: 32972665
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.
    Pan X; Jin K; Cao J; Liu Z; Wu J; You K; Lu Y; Xu Y; Su Z; Jiang J; Yao K; Ye J
    Graefes Arch Clin Exp Ophthalmol; 2020 Apr; 258(4):779-785. PubMed ID: 31932886
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Effective methods of diabetic retinopathy detection based on deep convolutional neural networks.
    Gu Y; Wang X; Pan J; Yong Z; Guo S; Pan T; Jiao Y; Zhou Z
    Int J Comput Assist Radiol Surg; 2021 Dec; 16(12):2177-2187. PubMed ID: 34606059
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Performance of deep neural network-based artificial intelligence method in diabetic retinopathy screening: a systematic review and meta-analysis of diagnostic test accuracy.
    Wang S; Zhang Y; Lei S; Zhu H; Li J; Wang Q; Yang J; Chen S; Pan H
    Eur J Endocrinol; 2020 Jun; 183(1):41-49. PubMed ID: 32504495
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning.
    Sugeno A; Ishikawa Y; Ohshima T; Muramatsu R
    Comput Biol Med; 2021 Oct; 137():104795. PubMed ID: 34488028
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Performance Analysis of Deep-Neural-Network-Based Automatic Diagnosis of Diabetic Retinopathy.
    Tariq H; Rashid M; Javed A; Zafar E; Alotaibi SS; Zia MYI
    Sensors (Basel); 2021 Dec; 22(1):. PubMed ID: 35009747
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.
    Hua CH; Huynh-The T; Kim K; Yu SY; Le-Tien T; Park GH; Bang J; Khan WA; Bae SH; Lee S
    Int J Med Inform; 2019 Dec; 132():103926. PubMed ID: 31605882
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Non-uniform Label Smoothing for Diabetic Retinopathy Grading from Retinal Fundus Images with Deep Neural Networks.
    Galdran A; Chelbi J; Kobi R; Dolz J; Lombaert H; Ben Ayed I; Chakor H
    Transl Vis Sci Technol; 2020 Jun; 9(2):34. PubMed ID: 32832207
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Vision Transformer-based recognition of diabetic retinopathy grade.
    Wu J; Hu R; Xiao Z; Chen J; Liu J
    Med Phys; 2021 Dec; 48(12):7850-7863. PubMed ID: 34693536
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.
    Gardner GG; Keating D; Williamson TH; Elliott AT
    Br J Ophthalmol; 1996 Nov; 80(11):940-4. PubMed ID: 8976718
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.
    Mohanty C; Mahapatra S; Acharya B; Kokkoras F; Gerogiannis VC; Karamitsos I; Kanavos A
    Sensors (Basel); 2023 Jun; 23(12):. PubMed ID: 37420891
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.