Detailed Information

Cited 0 time in webofscience Cited 9 time in scopus
Metadata Downloads

StoolNet for Color Classification of Stool Medical Images

Full metadata record
DC Field Value Language
dc.contributor.authorYang, Ziyuan-
dc.contributor.authorLeng, Lu-
dc.contributor.authorKim, Byung-Gyu-
dc.date.available2021-02-22T05:36:22Z-
dc.date.issued2019-12-
dc.identifier.issn1450-5843-
dc.identifier.issn2079-9292-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/2705-
dc.description.abstractThe color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians' heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.-
dc.language영어-
dc.language.isoENG-
dc.publisherMDPI-
dc.titleStoolNet for Color Classification of Stool Medical Images-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/electronics8121464-
dc.identifier.scopusid2-s2.0-85075929412-
dc.identifier.wosid000506678200091-
dc.identifier.bibliographicCitationELECTRONICS, v.8, no.12-
dc.citation.titleELECTRONICS-
dc.citation.volume8-
dc.citation.number12-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordPlusENHANCEMENT-
dc.subject.keywordAuthorStoolNet-
dc.subject.keywordAuthorconvolutional neural network-
dc.subject.keywordAuthorcolor classification-
dc.subject.keywordAuthorstool medical image-
dc.identifier.urlhttps://www.mdpi.com/2079-9292/8/12/1464-
Files in This Item
Go to Link
Appears in
Collections
ICT융합공학부 > IT공학전공 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kim, Byung Gyu photo

Kim, Byung Gyu
공과대학 (인공지능공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE