Bleeding and Ulcer Detection Using Wireless Capsule Endoscopy Images

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Journal of Software Engineering and Applications

ISSN Print: 1945-3116
ISSN Online: 1945-3124
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"Bleeding and Ulcer Detection Using Wireless Capsule Endoscopy Images"
written by Jinn-Yi Yeh, Tai-Hsi Wu, Wei-Jun Tsai,
published by Journal of Software Engineering and Applications, Vol.7 No.5, 2014

has been cited by the following article(s):

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CrossRef

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[4]	Ulcer and Red Lesion Detection in Wireless Capsule Endoscopy Images using CNN
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[5]	Time-based self-supervised learning for Wireless Capsule Endoscopy
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[11]	3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks
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[12]	Computer-aided diagnosis of gastrointestinal ulcer and hemorrhage using wireless capsule endoscopy: systematic review and diagnostic test accuracy meta …
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[13]	A Study on Wireless Capsule Endoscopy for Small Intestinal Lesions Detection Based on Deep Learning Target Detection
	2020

[14]	Convenient Way to Detect Ulcer in Wireless Capsule Endoscopy Through Fuzzy Logic Technique
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[15]	Ulcer Detection in Wireless Capsule Endoscopy Using Locally Computed Features
	2020

[16]	CrohnIPI: An endoscopic image database for the evaluation of automatic Crohn's disease lesions recognition algorithms
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[17]	StomachNet: optimal deep learning features fusion for stomach abnormalities classification
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[18]	Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images
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[19]	Ulcer Detection in Image Converted from Video Footage of Wireless Capsule Endoscopy
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[20]	Gastrointestinal diagnosis using non-white light imaging capsule endoscopy
	Nature Reviews Gastroenterology & Hepatology, 2019

[21]	Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images
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[22]	Bleeding detection in wireless capsule endoscopy videos—Color versus texture features
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[23]	Second glance framework (secG): enhanced ulcer detection with deep learning on a large wireless capsule endoscopy dataset
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[24]	Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy
	Sensors, 2019

[25]	Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization
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[26]	A locally based feature descriptor for abnormalities detection
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[27]	A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks
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[28]	Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy images
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[29]	Discretion Way for Bleeding Detection in Wireless Capsule Endoscopy Images
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[30]	Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images
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[31]	EMPIRICAL COMPARISON OF VISUAL DESCRIPTORS FOR ULCER RECOGNITION IN WIRELESS CAPSULE ENDOSCOPY VIDEO
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[32]	Real-time identification of blood regions for hemostasis support in laparoscopic surgery
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[33]	Multiple bleeding detection in wireless capsule endoscopy
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[34]	Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images
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[35]	Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images
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[36]	A hybrid video mining approach for Cancerous polyp detection in endoscopy videos
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[37]	Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images Computer-aided detection of small intestinal ulcer and …
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[38]	Computer-aided system for disease detection in wireless capsule endoscopy images
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[39]	Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images
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[40]	A fully automated ulcer detection system for wireless capsule endoscopy images
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[41]	Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy videos
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[42]	Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process
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[43]	Generic Feature Learning for Wireless Capsule Endoscopy Analysis
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[44]	Automatic Small Intestinal Ulcer Detection in Capsule Endoscopy Images
	International Journal of Scientific & Engineering Research, 2016

[45]	Ulcer Detection and Classification of Wireless Capsule Endoscopy Images Using RGB Masking
	Advanced Science Letters, 2016

[46]	Comparison of computer aided diagnostic system for wireless capsule endoscopy images using various filters
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[47]	Study on Anomaly Image Detection for Capsule Endoscopy Diagnosis Support
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[48]	Customizable 3-D Virtual GI Tract Systems For Locating, Mapping, And Navigation Inside Human Gastrointestinal Tract
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[49]	Mining Breast Cancer Classification Rules from Mammograms
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[50]	Determination of ulcer in the digestive tract using image analysis in wireless capsule endoscopy
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[51]	A new method for ulcer detection in endoscopic images
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[5]	High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention Bioengineering, 2023 DOI:10.3390/bioengineering10121416

[6]	Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks Sensors, 2023 DOI:10.3390/s23249717

[7]	Contrastive Self-Supervised Learning on Crohn’s Disease Detection 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022 DOI:10.1109/BIBM55620.2022.9995504

[8]	Contrastive Self-Supervised Learning on Crohn’s Disease Detection 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022 DOI:10.1109/BIBM55620.2022.9995504

[9]	AUTOMATED POLYP DETECTION IN COLONOSCOPY VIDEOS USING IMAGE ENHANCEMENT AND SALIENCY DETECTION ALGORITHM Biomedical Engineering: Applications, Basis and Communications, 2022 DOI:10.4015/S1016237222500016

[10]	Lesion Segmentation in Gastroscopic Images Using Generative Adversarial Networks Journal of Digital Imaging, 2022 DOI:10.1007/s10278-022-00591-1

[11]	Time-based self-supervised learning for Wireless Capsule Endoscopy Computers in Biology and Medicine, 2022 DOI:10.1016/j.compbiomed.2022.105631

[12]	3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks Complex & Intelligent Systems, 2022 DOI:10.1007/s40747-021-00328-7

[13]	Robust Ulcer Classification: Contrast and Illumination Invariant Approach Diagnostics, 2022 DOI:10.3390/diagnostics12122898

[14]	A Real-Time Video Analysis Software Based on Deep Convolutional Neural Networks (DCNN) for Useful and Effective Endoscopy Video Storage 2021 3rd International Conference on Intelligent Medicine and Image Processing, 2021 DOI:10.1145/3468945.3468947

[15]	A Real-Time Video Analysis Software Based on Deep Convolutional Neural Networks (DCNN) for Useful and Effective Endoscopy Video Storage 2021 3rd International Conference on Intelligent Medicine and Image Processing, 2021 DOI:10.1145/3468945.3468947

[16]	Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review Current Medical Imaging Formerly Current Medical Imaging Reviews, 2021 DOI:10.2174/1573405616666200425220513

[17]	Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis Journal of Medical Internet Research, 2021 DOI:10.2196/33267

[18]	Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention Sensors, 2021 DOI:10.3390/s22010283

[19]	Cyber Security and Computer Science Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2020 DOI:10.1007/978-3-030-52856-0_39

[20]	A Study on Wireless Capsule Endoscopy for Small Intestinal Lesions Detection Based on Deep Learning Target Detection IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3019888

[21]	Convenient Way to Detect Ulcer in Wireless Capsule Endoscopy Through Fuzzy Logic Technique 2020 IEEE Region 10 Symposium (TENSYMP), 2020 DOI:10.1109/TENSYMP50017.2020.9231004

[22]	StomachNet: Optimal Deep Learning Features Fusion for Stomach Abnormalities Classification IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3034217

[23]	Gastrointestinal diagnosis using non-white light imaging capsule endoscopy Nature Reviews Gastroenterology & Hepatology, 2019 DOI:10.1038/s41575-019-0140-z

[24]	Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images Sensors, 2019 DOI:10.3390/s19061265

[25]	Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy images IET Image Processing, 2019 DOI:10.1049/iet-ipr.2018.6232

[26]	Bleeding detection in wireless capsule endoscopy videos - Color versus texture features Journal of Applied Clinical Medical Physics, 2019 DOI:10.1002/acm2.12662

[27]	A locally based feature descriptor for abnormalities detection Soft Computing, 2019 DOI:10.1007/s00500-019-04208-8

[28]	Second glance framework (secG): enhanced ulcer detection with deep learning on a large wireless capsule endoscopy dataset Fourth International Workshop on Pattern Recognition, 2019 DOI:10.1117/12.2540456

[29]	Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization Computational and Mathematical Methods in Medicine, 2019 DOI:10.1155/2019/7546215

[30]	Ulcer Detection in Image Converted from Video Footage of Wireless Capsule Endoscopy 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019 DOI:10.1109/ICASERT.2019.8934597

[31]	Discretion Way for Bleeding Detection in Wireless Capsule Endoscopy Images 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019 DOI:10.1109/ICASERT.2019.8934589

[32]	A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks Physics in Medicine & Biology, 2019 DOI:10.1088/1361-6560/ab5086

[33]	Bleeding detection in wireless capsule endoscopy videos — Color versus texture features Journal of Applied Clinical Medical Physics, 2019 DOI:10.1002/acm2.12662

[34]	Multi-scale completed local binary patterns for ulcer detection in wireless capsule endoscopy images Multimedia Tools and Applications, 2018 DOI:10.1007/s11042-018-6086-2

[35]	Multiple bleeding detection in wireless capsule endoscopy Signal, Image and Video Processing, 2018 DOI:10.1007/s11760-018-1336-3

[36]	Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images Physics in Medicine & Biology, 2018 DOI:10.1088/1361-6560/aad51c

[37]	Real-time identification of blood regions for hemostasis support in laparoscopic surgery Signal, Image and Video Processing, 2018 DOI:10.1007/s11760-018-1369-7

[38]	Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy videos 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2017 DOI:10.1109/ATSIP.2017.8075590

[39]	A fully automated ulcer detection system for wireless capsule endoscopy images 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2017 DOI:10.1109/ATSIP.2017.8075599

[40]	Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images Applied Sciences, 2017 DOI:10.3390/app7101097

[41]	Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process Medical Image Analysis, 2016 DOI:10.1016/j.media.2016.04.007

[42]	Generic feature learning for wireless capsule endoscopy analysis Computers in Biology and Medicine, 2016 DOI:10.1016/j.compbiomed.2016.10.011

[43]	A new method for ulcer detection in endoscopic images 2015 2nd International Conference on Electronics and Communication Systems (ICECS), 2015 DOI:10.1109/ECS.2015.7124881

[44]	Determination of ulcer in the digestive tract using image analysis in wireless capsule endoscopy Biomedical Spectroscopy and Imaging, 2015 DOI:10.3233/BSI-150124

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	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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