Deep Learning Based Gastro Intestinal Disease Analysis Using Wireless Capsule Endoscopy Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU147842" target="_blank" >RIV/00216305:26220/22:PU147842 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9851383" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9851383</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP55681.2022.9851383" target="_blank" >10.1109/TSP55681.2022.9851383</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning Based Gastro Intestinal Disease Analysis Using Wireless Capsule Endoscopy Images

Popis výsledku v původním jazyce

Accurate detection of gastrointestinal illnesses is decisive for early cancer diagnosis and its treatment. However, manual analysis is time-consuming and requires a professional gastroenterologist. An efficient, robust and light-weight multi-class classification framework is proposed for screening different gastrointestinal diseases. A shallow neural network is developed that can extract the discriminative features by convolution of wireless capsule endoscopy (WCE) image even though the diseased images share common patterns. The network is optimised with various optimisation techniques to get the most optimised classification network. The proposed framework is capableof handling the challenges present in the dataset to improve the efficacy of the classification network. The network diagnoses unseen WCE image with 90% accuracy. The developed architecture is compared with other state-of-the-art networks and found to be highly efficient. The proposed network has the potential to perform better in limited computation and resource requirements.

Název v anglickém jazyce

Deep Learning Based Gastro Intestinal Disease Analysis Using Wireless Capsule Endoscopy Images

Popis výsledku anglicky

Accurate detection of gastrointestinal illnesses is decisive for early cancer diagnosis and its treatment. However, manual analysis is time-consuming and requires a professional gastroenterologist. An efficient, robust and light-weight multi-class classification framework is proposed for screening different gastrointestinal diseases. A shallow neural network is developed that can extract the discriminative features by convolution of wireless capsule endoscopy (WCE) image even though the diseased images share common patterns. The network is optimised with various optimisation techniques to get the most optimised classification network. The proposed framework is capableof handling the challenges present in the dataset to improve the efficacy of the classification network. The network diagnoses unseen WCE image with 90% accuracy. The developed architecture is compared with other state-of-the-art networks and found to be highly efficient. The proposed network has the potential to perform better in limited computation and resource requirements.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

TSP 2022: 2022 45th International Conference on Telecommunications and Signal Processing

ISBN

9781665469487

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

221-225

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

neuveden

Místo konání akce

Prague

Datum konání akce

13. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001070846300045