CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Ensemble-LungMaskNet: Automated lung segmentation using ensembled deep encoders
Authors
Mehmet Kemal Güllü
Oğuzhan Urhan
Coşku Öksüz
Publication date
1 January 2021
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
Kocaeli University;Kocaeli University Technopark2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- -- 172175Automated lung segmentation has importance because it gives clues about several diseases to the experts. It is the step that comes before further detailed analyses of the lungs. However, segmentation of the lungs is a challenging task since the opacities and consolidations are caused by various lung diseases. As a result, the clarity of the borders of the lungs may be lost which makes the segmentation task difficult. The presence of various medical equipment such as cables in the image is another factor that makes segmentation difficult. Therefore, it is a necessity to develop methods that can handle such situations. Learning the most useful patterns related to various diseases is possible with deep learning methods. Unlike conventional methods, learning the patterns improves the generalization ability of the models on unseen data. For this purpose, a deep segmentation framework including ensembles of pre-trained lightweight networks is proposed for lung region segmentation in this work. The experimental results achieved on two publicly available data sets demonstrate the effectiveness of the proposed framework. © 2021 IEEE
Similar works
Full text
Available Versions
Bakircay University Institutional Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:acikerisim.bakircay.edu.tr...
Last time updated on 23/08/2022