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Deep Convolutional Neural Networks for WCE Abnormality Detection: CNN Architecture, Region Proposal and Transfer Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/mp.12147openalex-snapshot
Deep learning for polyp recognition in wireless capsule endoscopy images
inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot
Visualizing and Understanding Convolutional Networks
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_26openalex-snapshot
Edge Boxes: Locating Object Proposals from Edges
inferredcites →https://doi.org/10.1007/978-3-642-40763-5_51openalex-snapshot
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot
The Pascal Visual Object Classes (VOC) Challenge
inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot
Selective Search for Object Recognition
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.bspc.2019.101792openalex-snapshot
Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network
inferredcites →https://doi.org/10.1016/j.cmpb.2012.09.004openalex-snapshot
Texture and color based image segmentation and pathology detection in capsule endoscopy videos
inferredcites →https://doi.org/10.1016/j.cmpb.2017.09.005openalex-snapshot
A deep learning-based multi-model ensemble method for cancer prediction
inferredcites →https://doi.org/10.1016/j.compbiomed.2016.10.011openalex-snapshot
Generic feature learning for wireless capsule endoscopy analysis
inferredcites →https://doi.org/10.1016/j.eswa.2012.03.029openalex-snapshot
Automatic polyp detection for wireless capsule endoscopy images
inferredcites →https://doi.org/10.1016/j.neuroimage.2014.12.061openalex-snapshot
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
inferredcites →https://doi.org/10.1016/s0016-5107(02)70037-0openalex-snapshot
Capsule endoscopy in the evaluation of patients with suspected small intestinal bleeding: Results of a pilot study
inferredcites →https://doi.org/10.1016/s0016-5107(03)02862-1openalex-snapshot
A prospective comparison of capsule endoscopy and push enteroscopy in patients with GI bleeding of obscure origin
inferredcites →https://doi.org/10.1016/s0016-5107(05)00372-xopenalex-snapshot
A prospective two-center study comparing wireless capsule endoscopy with intraoperative enteroscopy in patients with obscure GI bleeding
inferredcites →https://doi.org/10.1038/nature21056openalex-snapshot
Dermatologist-level classification of skin cancer with deep neural networks
inferredcites →https://doi.org/10.1109/access.2017.2788044openalex-snapshot
Deep Learning Applications in Medical Image Analysis
inferredcites →https://doi.org/10.1109/access.2018.2885997openalex-snapshot
CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning
inferredcites →https://doi.org/10.1109/cbms.2017.13openalex-snapshot
Sparse Coded Handcrafted and Deep Features for Colon Capsule Video Summarization
inferredcites →https://doi.org/10.1109/cvpr.2011.5995344openalex-snapshot
Global contrast based salient region detection
inferredcites →https://doi.org/10.1109/cvpr.2014.414openalex-snapshot
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.92openalex-snapshot
LocNet: Improving Localization Accuracy for Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2016.98openalex-snapshot
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
inferredcites →https://doi.org/10.1109/embc.2016.7590783openalex-snapshot
A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images
inferredcites →https://doi.org/10.1109/embc.2017.8037526openalex-snapshot
Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features
inferredcites →https://doi.org/10.1109/icassp.2006.1660413openalex-snapshot
Colour and Texture Based Gastrointestinal Tissue Discrimination
inferredcites →https://doi.org/10.1109/iccv.2011.6126456openalex-snapshot
Segmentation as selective search for object recognition
inferredcites →https://doi.org/10.1109/iccv.2015.135openalex-snapshot
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
inferredcites →https://doi.org/10.1109/iccv.2015.285openalex-snapshot
DeepBox: Learning Objectness with Convolutional Networks
inferredcites →https://doi.org/10.1109/iccv.2015.296openalex-snapshot
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
inferredcites →https://doi.org/10.1109/icdsp.2015.7252086openalex-snapshot
Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network
inferredcites →https://doi.org/10.1109/icme.2017.8019415openalex-snapshot
Convolutional neural networks for intestinal hemorrhage detection in wireless capsule endoscopy images
inferredcites →https://doi.org/10.1109/isbi.2017.7950698openalex-snapshot
A convolutional neural network approach for abnormality detection in Wireless Capsule Endoscopy
inferredcites →https://doi.org/10.1109/jbhi.2015.2399502openalex-snapshot
Bleeding Frame and Region Detection in the Wireless Capsule Endoscopy Video
inferredcites →https://doi.org/10.1109/jbhi.2016.2636929openalex-snapshot
Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis
inferredcites →https://doi.org/10.1109/robio.2015.7419037openalex-snapshot
A hybrid convolutional neural networks with extreme learning machine for WCE image classification
inferredcites →https://doi.org/10.1109/spmb.2017.8257027openalex-snapshot
Superpixel based segmentation and classification of polyps in wireless capsule endoscopy
inferredcites →https://doi.org/10.1109/tase.2015.2395429openalex-snapshot
Improved Bag of Feature for Automatic Polyp Detection in Wireless Capsule Endoscopy Images
inferredcites →https://doi.org/10.1109/tase.2016.2610579openalex-snapshot
WCE Abnormality Detection Based on Saliency and Adaptive Locality-Constrained Linear Coding
inferredcites →https://doi.org/10.1109/tbme.2011.2155064openalex-snapshot
Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos
inferredcites →https://doi.org/10.1109/tbme.2011.2172438openalex-snapshot
Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images
inferredcites →https://doi.org/10.1109/tbme.2017.2712771openalex-snapshot
Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks
inferredcites →https://doi.org/10.1109/tcsvt.2006.873158openalex-snapshot
MPEG-7 Visual Descriptors—Contributions for Automated Feature Extraction in Capsule Endoscopy
inferredcites →https://doi.org/10.1109/tcyb.2017.2726818openalex-snapshot
Discriminative Joint-Feature Topic Model With Dual Constraints for WCE Classification
inferredcites →https://doi.org/10.1109/tfuzz.2016.2574915openalex-snapshot
A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification
inferredcites →https://doi.org/10.1109/tip.2015.2487833openalex-snapshot
Salient Object Detection: A Benchmark
inferredcites →https://doi.org/10.1109/tip.2016.2588330openalex-snapshot
Deep and Structured Robust Information Theoretic Learning for Image Analysis
inferredcites →https://doi.org/10.1109/tip.2018.2801119openalex-snapshot
Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning
inferredcites →https://doi.org/10.1109/titb.2012.2185807openalex-snapshot
Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection
inferredcites →https://doi.org/10.1109/tmi.2008.926061openalex-snapshot
Wireless Capsule Endoscopy Color Video Segmentation
inferredcites →https://doi.org/10.1109/tmi.2015.2418534openalex-snapshot
Saliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis
inferredcites →https://doi.org/10.1109/tmi.2016.2528162openalex-snapshot
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
inferredcites →https://doi.org/10.1109/tmi.2016.2535302openalex-snapshot
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
inferredcites →https://doi.org/10.1109/tpami.2012.28openalex-snapshot
Measuring the Objectness of Image Windows
inferredcites →https://doi.org/10.1109/tpami.2014.2345401openalex-snapshot
Global Contrast Based Salient Region Detection
inferredcites →https://doi.org/10.1109/tpami.2014.2377715openalex-snapshot
Fast Edge Detection Using Structured Forests
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/tpami.2015.2437384openalex-snapshot
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
inferredcites →https://doi.org/10.1109/tpami.2015.2465908openalex-snapshot
What Makes for Effective Detection Proposals?
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.48550/arxiv.1406.6962openalex-snapshot
How good are detection proposals, really?
inferredcites →https://doi.org/10.48550/arxiv.1408.5093openalex-snapshot
Caffe: Convolutional Architecture for Fast Feature Embedding
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1606.05718openalex-snapshot
Deep Learning for Identifying Metastatic Breast Cancer
inferredcites →https://doi.org/10.48550/arxiv.1711.05225openalex-snapshot
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
inferredcites →https://doi.org/10.5244/c.28.24openalex-snapshot
How good are detection proposals, really?
inferredcites →https://doi.org/10.7555/jbr.31.20160008openalex-snapshot
Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system
inferredcites →https://doi.org/10.7726/jac.2014.1002aopenalex-snapshot
Informative and Uninformative Regions Detection in WCE Frames
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4253902openalex-extracted
A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images
inferred← citeshttps://doi.org/10.1002/ima.70121openalex-snapshot
Explainable Weakly Supervised Deep Learning Model for Anomaly Classification and Localization in Wireless Capsule Endoscopy Images
inferred← citeshttps://doi.org/10.1002/rcs.70094openalex-snapshot
A Review on the Current Research Status of Key Areas in Wireless Capsule Endoscopy
inferred← citeshttps://doi.org/10.1007/s10462-021-10127-8openalex-snapshot
A complete framework for accurate recognition and prognosis of COVID-19 patients based on deep transfer learning and feature classification approach
inferred← citeshttps://doi.org/10.1007/s11063-021-10481-2openalex-snapshot
Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization
inferred← citeshttps://doi.org/10.1007/s12652-021-03296-5openalex-snapshot
A region proposal algorithm using texture similarity and perceptual grouping
inferred← citeshttps://doi.org/10.1007/s40747-023-01271-5openalex-snapshot
Multi-classification deep learning models for detection of ulcerative colitis, polyps, and dyed-lifted polyps using wireless capsule endoscopy images
inferred← citeshttps://doi.org/10.1007/s40846-022-00686-8openalex-snapshot
Attention Aware Deep Learning Model for Wireless Capsule Endoscopy Lesion Classification and Localization
inferred← citeshttps://doi.org/10.1007/s40846-023-00815-xopenalex-snapshot
A Convolutional Neural Network with Meta-feature Learning for Wireless Capsule Endoscopy Image Classification
inferred← citeshttps://doi.org/10.1016/j.bbe.2021.11.004openalex-snapshot
Transfer learning techniques for medical image analysis: A review
inferred← citeshttps://doi.org/10.1016/j.bspc.2020.101897openalex-snapshot
Automatic reduction of wireless capsule endoscopy reviewing time based on factorization analysis
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111659openalex-snapshot
Machine learning methodologies in video capsule endoscopy-based bleeding analysis: A systematic review of progress and prospects (2008–2024)
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.116105openalex-snapshot
A comprehensive and systematic review on classical and deep learning based region proposal algorithms
inferred← citeshttps://doi.org/10.1016/j.future.2020.06.048openalex-snapshot
Vision-based personalized Wireless Capsule Endoscopy for smart healthcare: Taxonomy, literature review, opportunities and challenges
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e41137openalex-snapshot
Deep learning-based object detection algorithms in medical imaging: Systematic review
inferred← citeshttps://doi.org/10.1016/j.isci.2025.114545openalex-snapshot
Multimodal-multiscale hybrid fusion network for automated esophageal cancer diagnosis
inferred← citeshttps://doi.org/10.1016/j.knosys.2021.106971openalex-snapshot
Recurrent generative adversarial networks for unsupervised WCE video summarization
inferred← citeshttps://doi.org/10.1016/j.media.2020.101733openalex-snapshot
Semi-supervised WCE image classification with adaptive aggregated attention
inferred← citeshttps://doi.org/10.1016/j.patrec.2019.12.024openalex-snapshot
Gastrointestinal diseases segmentation and classification based on duo-deep architectures
inferred← citeshttps://doi.org/10.1049/iet-ipr.2019.0415openalex-snapshot
Multi‐scale analysis of ulcer disease detection from WCE images
inferred← citeshttps://doi.org/10.1049/ipr2.12704openalex-snapshot
WCE‐DCGAN: A data augmentation method based on wireless capsule endoscopy images for gastrointestinal disease detection
inferred← citeshttps://doi.org/10.1109/access.2019.2944676openalex-snapshot
Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images
inferred← citeshttps://doi.org/10.1109/eit57321.2023.10187250openalex-snapshot
Gastrointestinal Disease Diagnosis with Hybrid Model of Capsules and CNNs
inferred← citeshttps://doi.org/10.1109/icce70609.2026.11658420openalex-snapshot
Machine Learning Based 6-DoF Electromagnetic Localization for Wireless Capsule Endoscopy
inferred← citeshttps://doi.org/10.1109/isbi45749.2020.9098634openalex-snapshot
Fully-Automated Semantic Segmentation of Wireless Capsule Endoscopy Abnormalities
inferred← citeshttps://doi.org/10.1109/jbhi.2021.3067789openalex-snapshot
Multi-Class Skin Lesion Detection and Classification via Teledermatology
inferred← citeshttps://doi.org/10.1109/jproc.2019.2950506openalex-snapshot
Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep-Learning-Based Polyp Recognition
inferred← citeshttps://doi.org/10.1111/1556-4029.15319openalex-snapshot
Automated detection of regions of interest in cartridge case images using deep learning
inferred← citeshttps://doi.org/10.1145/3436829.3436837openalex-snapshot
Ulcer Recognition based on 6-Layers Deep Convolutional Neural Network
inferred← citeshttps://doi.org/10.1155/2023/8616939openalex-snapshot
Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features
inferred← citeshttps://doi.org/10.14569/ijacsa.2023.0140971openalex-snapshot
Deep Learning-based Multiple Bleeding Detection in Wireless Capsule Endoscopy
inferred← citeshttps://doi.org/10.17762/ijritcc.v11i9.10083openalex-snapshot
Intelligent Hemorrhage Identification in Wireless Capsule Endoscopy Pictures Using AI Techniques.
inferred← citeshttps://doi.org/10.21928/uhdjst.v4n2y2020.pp75-90openalex-snapshot
Review Research of Medical Image Analysis Using Deep Learning
inferred← citeshttps://doi.org/10.2196/preprints.47462openalex-snapshot
Object Detection in Medical Imaging: A Quantitative and Qualitative Analysis of the Scientific Research (Preprint)
inferred← citeshttps://doi.org/10.2298/csis201030049sopenalex-snapshot
Explainable information retrieval using deep learning for medical images
inferred← citeshttps://doi.org/10.32604/cmc.2021.015541openalex-snapshot
Classification of COVID-19 CT Scans via Extreme Learning Machine
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A Two Stream Fusion Assisted Deep Learning Framework for Stomach Diseases Classification
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Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches
inferred← citeshttps://doi.org/10.3390/app12010209openalex-snapshot
Enhanced Image Captioning with Color Recognition Using Deep Learning Methods
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A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images
inferred← citeshttps://doi.org/10.3390/s22114079openalex-snapshot
Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases
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Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques
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