Polyp detection in Wireless Capsule Endoscopy images by using region-based active contour model

2012 ◽  
Author(s):  
Hoda Eskandari ◽  
Alireza Talebpour ◽  
Mahdi Alizadeh ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Capsule Endoscopy ◽  
Active Contour ◽  
Active Contour Model ◽  
Wireless Capsule Endoscopy ◽  
Polyp Detection ◽  
Contour Model ◽  
Wireless Capsule

scholarly journals Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps

2019 ◽  
Vol 9 (12) ◽  
pp. 2404 ◽  
Author(s):  
Sudhir Sornapudi ◽  
Frank Meng ◽  
Steven Yi
Keyword(s):  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Automated Detection ◽  
Fine Tuning ◽  
Screening Tests ◽  
Polyp Detection ◽  
False Negatives ◽  
Polyp Detection Rate ◽  
Manual Examination ◽  
Wireless Capsule

The early detection of polyps could help prevent colorectal cancer. The automated detection of polyps on the colon walls could reduce the number of false negatives that occur due to manual examination errors or polyps being hidden behind folds, and could also help doctors locate polyps from screening tests such as colonoscopy and wireless capsule endoscopy. Losing polyps may result in lesions evolving badly. In this paper, we propose a modified region-based convolutional neural network (R-CNN) by generating masks around polyps detected from still frames. The locations of the polyps in the image are marked, which assists the doctors examining the polyps. The features from the polyp images are extracted using pre-trained Resnet-50 and Resnet-101 models through feature extraction and fine-tuning techniques. Various publicly available polyp datasets are analyzed with various pertained weights. It is interesting to notice that fine-tuning with balloon data (polyp-like natural images) improved the polyp detection rate. The optimum CNN models on colonoscopy datasets including CVC-ColonDB, CVC-PolypHD, and ETIS-Larib produced values (F1 score, F2 score) of (90.73, 91.27), (80.65, 79.11), and (76.43, 78.70) respectively. The best model on the wireless capsule endoscopy dataset gave a performance of (96.67, 96.10). The experimental results indicate the better localization of polyps compared to recent traditional and deep learning methods.

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