Automatic Colon Polyp Detection Using Region Based Deep CNN and Post Learning Approaches
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TLDR
A recent region-based convolutional neural network (CNN) approach is applied for the automatic detection of polyps in the images and videos obtained from colonoscopy examinations using a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system.Abstract:
Automatic image detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this paper, we apply a recent region-based convolutional neural network (CNN) approach for the automatic detection of polyps in the images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods, such as automatic false positive learning and offline learning, both of which can be incorporated with the region-based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance than other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.read more
Citations
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Journal ArticleDOI
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
Debesh Jha,Sharib Ali,Nikhil Kumar Tomar,Håvard D. Johansen,Dag Johansen,Jens Rittscher,Michael Riegler,Pål Halvorsen +7 more
TL;DR: A comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.
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Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer
R.A. Welikala,Paolo Remagnino,Jian Han Lim,Chee Seng Chan,Senthilmani Rajendran,Thomas George Kallarakkal,Rosnah Binti Zain,Ruwan Duminda Jayasinghe,Jyotsna Rimal,Alexander Ross Kerr,Rahmi Amtha,Karthikeya Patil,Wanninayake Mudiyanselage Tilakaratne,John Gibson,Sok Ching Cheong,Sarah Barman +15 more
TL;DR: Two deep learning based computer vision approaches were assessed for the automated detection and classification of oral lesions for the early detection of oral cancer, these were image classification with ResNet-101 and object detection with the Faster R-CNN.
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A comprehensive review of deep learning in colon cancer.
TL;DR: An overview of popular deep learning architectures used in colon cancer analysis is presented, including 135 recent academic papers, separating colon cancer into five different classes, and providing a comprehensive structure.
Journal ArticleDOI
Deep learning to find colorectal polyps in colonoscopy: A systematic literature review.
Luisa F. Sánchez-Peralta,Luis Bote-Curiel,Artzai Picon,Francisco M. Sánchez-Margallo,J. Blas Pagador +4 more
TL;DR: An analysis of the proposed methods for polyp detection, localization and segmentation, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations.
Journal ArticleDOI
Improving Automatic Polyp Detection Using CNN by Exploiting Temporal Dependency in Colonoscopy Video
Hemin Ali Qadir,Ilangko Balasingham,Johannes Solhusvik,Jacob Bergsland,Lars Aabakken,Younghak Shin +5 more
TL;DR: This method provides an overall performance improvement in terms of sensitivity, precision, and specificity compared to conventional false positive learning method, and thus achieves the state-of-the-art results on the CVC-ClinicVideoDB video data set.
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