Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges faced during manual inspection of long WCE videos. Several approaches have been proposed in the literature for the automatic detection and localization of anomalies in WCE images. Some of them focus on specific anomalies such as bleeding, polyp, lesion, etc. However, relatively fewer generic methods have been proposed to detect all those common anomalies simultaneously. In this paper, a deep convolutional neural network (CNN) based model 'WCENet' is proposed for anomaly detection and localization in WCE images. The model works in two phases. In the first phase, a simple and efficient attention-based CNN classifies an image into one of the four categories: polyp, vascular, inflammatory, or normal. If the image is classified in one of the abnormal categories, it is processed in the second phase for the anomaly localization. Fusion of Grad-CAM++ and a custom SegNet is used for anomalous region segmentation in the abnormal image. WCENet classifier attains accuracy and area under receiver operating characteristic of 98% and 99%. The WCENet segmentation model obtains a frequency weighted intersection over union of 81%, and an average dice score of 56% on the KID dataset. WCENet outperforms nine different state-of-the-art conventional machine learning and deep learning models on the KID dataset. The proposed model demonstrates potential for clinical applications.

2nd Department of Internal Medicine Gastroenterology Charles University Faculty of Medicine in Hradec Kralove and University Hospital Hradec Kralove Sokolska 581 Hradec Kralove 50005 Czech Republic

Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Hradecka 1249 Hradec Kralove 50003 Czech Republic; Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra 54100 Kuala Lumpur Malaysia

Department of Computer Science OsloMet Oslo Metropolitan University Oslo Norway; Department of Plastic and Reconstructive Surgery Oslo University Hospital Oslo Norway; Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005 India

Citace poskytuje Crossref.org