Deep Learning Based Gastro Intestinal Disease Analysis Using Wireless Capsule Endoscopy Images
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Deep Learning Based Gastro Intestinal Disease Analysis Using Wireless Capsule Endoscopy Images
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
TSP 2022: 2022 45th International Conference on Telecommunications and Signal Processing
ISBN
9781665469487
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
221-225
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
neuveden
Event location
Prague
Event date
Jul 13, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001070846300045