A deep convolutional neural network for the detection of polyps in colonoscopy images
TLDR
A deep convolutional neural network based model for the computerized detection of polyps within colonoscopy images, using a generalized intersection of union, thus overcoming issues such as scale invariance, rotation, and shape.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2021-07-01 and is currently open access. It has received 31 citations till now. The article focuses on the topics: Convolutional neural network & Softmax function.read more
Citations
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Journal ArticleDOI
A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images.
TL;DR: In this article, a deep convolutional neural network (CNN) based model, WCENet, is proposed for anomaly detection and localization in wireless capsule endoscopy (WCE) images.
Journal ArticleDOI
An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
TL;DR: In this paper, a CNN model with 13 convolutional layers, a softmax layer as an output layer, and two fully connected layers (FCN) are constructed to detect road cracks, potholes, and the yellow lane.
Posted Content
An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
TL;DR: An attempt is made in this study to use a deep learning (DL) approach for the early detection of road cracks, potholes, and the yellow lane to enhance accuracy and prevent saturation in the training phase.
Journal ArticleDOI
Towards a better understanding of annotation tools for medical imaging: a survey
Manar Ibrahim Aljabri,Manal Alamir,Manal Al Ghamdi,Mohamed Abdel-Mottaleb,Fernando Collado-Mesa +4 more
TL;DR: In this article , the authors present a survey of the currently available annotation tools for medical imaging, including descriptions of graphical user interfaces (GUI) and supporting instruments, and show their successful usage in annotating medical imaging dataset to guide researchers in this area.
Journal ArticleDOI
Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture
J. S. Nisha,P. Palanisamy +1 more
TL;DR: In this article , a Dual-Path Convolutional Neural Network (DP-CNN) was proposed to classify polyp and non-polyp patches from the colonoscopy images.
References
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Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
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