{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:53:27Z","timestamp":1780088007013,"version":"3.54.0"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T00:00:00Z","timestamp":1568764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Project from Ankon Technologies Co. Ltd.","award":["2013YQ160439"],"award-info":[{"award-number":["2013YQ160439"]}]},{"name":"Research Project from Ankon Technologies Co. Ltd.","award":["ZJ2017-ZD-001"],"award-info":[{"award-number":["ZJ2017-ZD-001"]}]},{"DOI":"10.13039\/501100012149","name":"National Key Scientific Instrument and Equipment Development Project","doi-asserted-by":"crossref","award":["2013YQ160439"],"award-info":[{"award-number":["2013YQ160439"]}],"id":[{"id":"10.13039\/501100012149","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012149","name":"National Key Scientific Instrument and Equipment Development Project","doi-asserted-by":"crossref","award":["ZJ2017-ZD-001"],"award-info":[{"award-number":["ZJ2017-ZD-001"]}],"id":[{"id":"10.13039\/501100012149","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhangjiang National Innovation Demonstration Zone Special Development Fund","award":["2013YQ160439"],"award-info":[{"award-number":["2013YQ160439"]}]},{"name":"Zhangjiang National Innovation Demonstration Zone Special Development Fund","award":["ZJ2017-ZD-001"],"award-info":[{"award-number":["ZJ2017-ZD-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2019,9,18]]},"abstract":"<jats:p>Wireless capsule endoscopy (WCE) has developed rapidly over the last several years and now enables physicians to examine the gastrointestinal tract without surgical operation. However, a large number of images must be analyzed to obtain a diagnosis. Deep convolutional neural networks (CNNs) have demonstrated impressive performance in different computer vision tasks. Thus, in this work, we aim to explore the feasibility of deep learning for ulcer recognition and optimize a CNN-based ulcer recognition architecture for WCE images. By analyzing the ulcer recognition task and characteristics of classic deep learning networks, we propose a HAnet architecture that uses ResNet-34 as the base network and fuses hyper features from the shallow layer with deep features in deeper layers to provide final diagnostic decisions. 1,416 independent WCE videos are collected for this study. The overall test accuracy of our HAnet is 92.05%, and its sensitivity and specificity are 91.64% and 92.42%, respectively. According to our comparisons of F1, F2, and ROC-AUC, the proposed method performs better than several off-the-shelf CNN models, including VGG, DenseNet, and Inception-ResNet-v2, and classical machine learning methods with handcrafted features for WCE image classification. Overall, this study demonstrates that recognizing ulcers in WCE images via the deep CNN method is feasible and could help reduce the tedious image reading work of physicians. Moreover, our HAnet architecture tailored for this problem gives a fine choice for the design of network structure.<\/jats:p>","DOI":"10.1155\/2019\/7546215","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T19:30:40Z","timestamp":1568835040000},"page":"1-14","source":"Crossref","is-referenced-by-count":59,"title":["Deep Convolutional Neural Network for Ulcer Recognition in Wireless Capsule Endoscopy: Experimental Feasibility and Optimization"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6948-3264","authenticated-orcid":true,"given":"Sen","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China"},{"name":"Department of Engineering Physics, Tsinghua University, Beijing 100084, 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