Fully automatic segmentation of gynaecological abnormality using a new Viola–Jones model

Vista sencilla de ítem
dc.contributor.authorHussein, Ihsan Jasim
dc.contributor.authorBurhanuddin, M.A.
dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorElhoseny, Mohamed
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorMaashi, Marwah Suliman
dc.contributor.authorMaashi, Mashael S.
dc.date.accessioned2026-03-13T10:09:40Z
dc.date.available2026-03-13T10:09:40Z
dc.date.issued2021
dc.date.updated2026-03-13T10:09:40Z
dc.description.abstractOne of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed segmentation approach. The breast cancer database contains 250 images, while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq. Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images. The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%. By contrast, the segmentation result of the proposed system in the ovarian tumor data set was 79.2%. In the classification results, we achieved 95.43% accuracy, 92.20% sensitivity, and 97.5% specificity when we used the breast cancer data set. For the ovarian tumor data set, we achieved 94.84% accuracy, 96.96% sensitivity, and 90.32% specificity.en
dc.description.sponsorshipThis research received funding from Basque Country Governmenten
dc.identifier.citationHussein, I. J., Burhanuddin, Mohammed, M. A., Elhoseny, M., Garcia-Zapirain, B., Maashi, M. S., & Maashi, M. S. (2021). Fully automatic segmentation of gynaecological abnormality using a new Viola–Jones model. Computers, Materials and Continua, 66(3), 3161-3182. https://doi.org/10.32604/CMC.2021.012691
dc.identifier.doi10.32604/CMC.2021.012691
dc.identifier.eissn1546-2226
dc.identifier.issn1546-2218
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5432
dc.language.isoeng
dc.publisherTech Science Press
dc.subject.otherActive contour
dc.subject.otherBreast cancer
dc.subject.otherBreast cancer segmentation
dc.subject.otherCascade model
dc.subject.otherOvarian tumor
dc.subject.otherOvarian tumor segmentation
dc.subject.otherUltrasound images
dc.subject.otherViola–Jones model
dc.titleFully automatic segmentation of gynaecological abnormality using a new Viola–Jones modelen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage3182
oaire.citation.issue3
oaire.citation.startPage3161
oaire.citation.titleComputers, Materials and Continua
oaire.citation.volume66
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
hussein_fully_2021.pdf
Tamaño:
1.12 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos