Repository logo
 
Publication

A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies

2018Journal article Open access
Simple item page
dc.contributor.authorCoelho, Paulo
dc.contributor.authorPereira, Ana
dc.contributor.authorLeite, Argentina
dc.contributor.authorSalgado, Marta
dc.contributor.authorCunha, António
dc.date.accessioned2018-11-13T17:12:12Z
dc.date.available2018-11-13T17:12:12Z
dc.date.issued2018
dc.description.abstractThe wireless capsule endoscopy has revolutionized early diagnosis of small bowel diseases. However, a single examination has up to 10 h of video and requires between 30–120 min to read. Computational methods are needed to increase both efficiency and accuracy of the diagnosis. In this paper, an evaluation of deep learning U-Net architecture is presented, to detect and segment red lesions in the small bowel. Its results were compared with those obtained from the literature review. To make the evaluation closer to those used in clinical environments, the U-Net was also evaluated in an annotated sequence by using the Suspected Blood Indicator tool (SBI). Results found that detection and segmentation using U-Net outperformed both the algorithms used in the literature review and the SBI tool.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCoelho P., Pereira A., Leite A., Salgado M., Cunha A. (2018) A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies. In: Campilho A., Karray F., ter Haar Romeny B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science, vol 10882. Springer, Champt_PT
dc.identifier.doihttps://doi.org/10.1007/978-3-319-93000-8_63pt_PT
dc.identifier.isbn978-3-319-92999-6
dc.identifier.urihttp://hdl.handle.net/10400.8/3645
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer, Champt_PT
dc.subjectLesion detectionpt_PT
dc.subjectGastrointestinal bleedingpt_PT
dc.subjectMachine learningpt_PT
dc.subjectCapsule endoscopypt_PT
dc.subjectDeep learningpt_PT
dc.subjectU-Netpt_PT
dc.subjectComputer Sciencept_PT
dc.subjectImage Analysispt_PT
dc.titleA Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopiespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage561pt_PT
oaire.citation.startPage553pt_PT
oaire.citation.volume10882pt_PT
person.familyNameCoelho
person.givenNamePaulo
person.identifier2068530
person.identifier.ciencia-id3818-FA4F-CC36
person.identifier.orcid0000-0002-4383-0472
person.identifier.ridV-1924-2018
person.identifier.scopus-author-id57128835100
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0a2d9abe-a60d-4c77-a1b7-ad0755f025bc
relation.isAuthorOfPublication.latestForDiscovery0a2d9abe-a60d-4c77-a1b7-ad0755f025bc

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A deep learning approach for redl esions detection in video capsule endoscopies.pdf
Size:
1.1 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.32 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESTG - Capítulos de livros