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 *

203 related articles for article (PubMed ID: 29060567)

  • 1. Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features.
    Xiao Jia ; Meng MQ
    Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():3154-3157. PubMed ID: 29060567
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images.
    Xiao Jia ; Meng MQ
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():639-642. PubMed ID: 28268409
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis.
    Qin K; Li J; Fang Y; Xu Y; Wu J; Zhang H; Li H; Liu S; Li Q
    Surg Endosc; 2022 Jan; 36(1):16-31. PubMed ID: 34426876
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Low Complexity CNN Structure for Automatic Bleeding Zone Detection in Wireless Capsule Endoscopy Imaging.
    Hajabdollahi M; Esfandiarpoor R; Najarian K; Karimi N; Samavi S; Reza Soroushmehr SM
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():7227-7230. PubMed ID: 31947501
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Bleeding detection in Wireless Capsule Endoscopy based on Probabilistic Neural Network.
    Pan G; Yan G; Qiu X; Cui J
    J Med Syst; 2011 Dec; 35(6):1477-84. PubMed ID: 20703770
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization.
    Wang S; Xing Y; Zhang L; Gao H; Zhang H
    Comput Math Methods Med; 2019; 2019():7546215. PubMed ID: 31641370
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computer-aided gastrointestinal hemorrhage detection in wireless capsule endoscopy videos.
    Hassan AR; Haque MA
    Comput Methods Programs Biomed; 2015 Dec; 122(3):341-53. PubMed ID: 26390947
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.
    Bordbar M; Helfroush MS; Danyali H; Ejtehadi F
    Biomed Eng Online; 2023 Dec; 22(1):124. PubMed ID: 38098015
    [TBL] [Abstract][Full Text] [Related]  

  • 9. BP neural network classification for bleeding detection in wireless capsule endoscopy.
    Pan G; Yan G; Song X; Qiu X
    J Med Eng Technol; 2009; 33(7):575-81. PubMed ID: 19639509
    [TBL] [Abstract][Full Text] [Related]  

  • 10. High pooled performance of convolutional neural networks in computer-aided diagnosis of GI ulcers and/or hemorrhage on wireless capsule endoscopy images: a systematic review and meta-analysis.
    Mohan BP; Khan SR; Kassab LL; Ponnada S; Chandan S; Ali T; Dulai PS; Adler DG; Kochhar GS
    Gastrointest Endosc; 2021 Feb; 93(2):356-364.e4. PubMed ID: 32721487
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images.
    Jain S; Seal A; Ojha A; Yazidi A; Bures J; Tacheci I; Krejcar O
    Comput Biol Med; 2021 Oct; 137():104789. PubMed ID: 34455302
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Bleeding detection in wireless capsule endoscopy videos - Color versus texture features.
    Pogorelov K; Suman S; Azmadi Hussin F; Saeed Malik A; Ostroukhova O; Riegler M; Halvorsen P; Hooi Ho S; Goh KL
    J Appl Clin Med Phys; 2019 Aug; 20(8):141-154. PubMed ID: 31251460
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Detection and Classification of Gastrointestinal Diseases using Machine Learning.
    Naz J; Sharif M; Yasmin M; Raza M; Khan MA
    Curr Med Imaging; 2021; 17(4):479-490. PubMed ID: 32988355
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection.
    Kundu AK; Fattah SA
    Comput Biol Med; 2019 Dec; 115():103478. PubMed ID: 31698239
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network.
    Saito H; Aoki T; Aoyama K; Kato Y; Tsuboi A; Yamada A; Fujishiro M; Oka S; Ishihara S; Matsuda T; Nakahori M; Tanaka S; Koike K; Tada T
    Gastrointest Endosc; 2020 Jul; 92(1):144-151.e1. PubMed ID: 32084410
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments.
    Li B; Meng MQ
    Comput Biol Med; 2009 Feb; 39(2):141-7. PubMed ID: 19147126
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Detection of small colon bleeding in wireless capsule endoscopy videos.
    Usman MA; Satrya GB; Usman MR; Shin SY
    Comput Med Imaging Graph; 2016 Dec; 54():16-26. PubMed ID: 27793502
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging.
    Rahim T; Usman MA; Shin SY
    Comput Med Imaging Graph; 2020 Oct; 85():101767. PubMed ID: 32966967
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An automatic bleeding detection scheme in wireless capsule endoscopy based on histogram of an RGB-indexed image.
    Ghosh T; Fattah SA; Shahnaz C; Wahid KA
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():4683-6. PubMed ID: 25571037
    [TBL] [Abstract][Full Text] [Related]  

  • 20. RAt-CapsNet: A Deep Learning Network Utilizing Attention and Regional Information for Abnormality Detection in Wireless Capsule Endoscopy.
    Alam MJ; Rashid RB; Fattah SA; Saquib M
    IEEE J Transl Eng Health Med; 2022; 10():3300108. PubMed ID: 36032311
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 11.