A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images. (October 2021)

• Record Type: Journal Article
• Authors: Jain, Samir; Seal, Ayan; Ojha, Aparajita; Yazidi, Anis; Bures, Jan; Tacheci, Ilja; Krejcar, Ondrej
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: Deep convolutional neural network -- Attention mechanism -- Wireless capsule endoscopy -- Anomaly detection -- Localization; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104789
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: A deep CNN model 'WCENet' is proposed for anomaly detection and localization in WCE images. An attention-based CNN is used to classify WCE images into four categories: polyp, vascular, inflammatory, and normal. A hybrid model consisting of a customized SegNet model and GradCAM++ segments anomaly present in the WCE image. WCENet performs better in the classification and localization of anomalies in comparison to state-of-the-art methods.