A deep convolutional neural network for the detection of polyps in colonoscopy images
Authors:
- Tariq RahimTRTariq Rahim
- Syed Ali HassanSHSyed Ali Hassan
- Soo Young ShinSSSoo Young Shin
- 1 July 2021
- journal article
- research article
- Published by Elsevier in Biomedical Signal Processing and Control
- Vol. 68
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- Ministry of Education, Science and Technology
- Ministry of Science, ICT and Future Planning (IITP-2021-2020-0-01612)
- National Research Foundation of Korea (2018R1A6A1A03024003)
This publication has 22 references indexed in Scilit:
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?IEEE Transactions on Medical Imaging, 2016
- Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer LearningIEEE Transactions on Medical Imaging, 2016
- Automated Polyp Detection in Colonoscopy Videos Using Shape and Context InformationIEEE Transactions on Medical Imaging, 2015
- WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physiciansComputerized Medical Imaging and Graphics, 2015
- Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural NetworksIEEE Journal of Biomedical and Health Informatics, 2015
- Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancerInternational Journal of Computer Assisted Radiology and Surgery, 2013
- Towards automatic polyp detection with a polyp appearance modelPattern Recognition, 2012
- Factors influencing the miss rate of polyps in a back-to-back colonoscopy studyEndoscopy, 2012
- Computer-aided tumor detection in endoscopic video using color wavelet featuresIEEE Transactions on Information Technology in Biomedicine, 2003