A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018283" target="_blank" >RIV/62690094:18450/21:50018283 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11150/21:10434839 RIV/00179906:_____/21:10434839

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0010482521005837" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0010482521005837</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2021.104789" target="_blank" >10.1016/j.compbiomed.2021.104789</a>

Result language

angličtina

Original language name

A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images

Original language description

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. © 2021 Elsevier Ltd

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF18_069%2F0010054" target="_blank" >EF18_069/0010054: IT4Neuro(degeneration)</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

—

Volume of the periodical

137

Issue of the periodical within the volume

October

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

"Article number: 104789"

UT code for WoS article

000703473200004

EID of the result in the Scopus database

2-s2.0-85113615106