Advanced
Please use this identifier to cite or link to this item: https://digital.lib.ueh.edu.vn/handle/UEH/61914
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKhamparia A.-
dc.contributor.otherBharati S.-
dc.contributor.otherPodder P.-
dc.contributor.otherGupta D.-
dc.contributor.otherKhanna A.-
dc.contributor.otherPhung T.K.-
dc.contributor.otherThanh D.N.H.-
dc.date.accessioned2021-08-20T14:47:58Z-
dc.date.available2021-08-20T14:47:58Z-
dc.date.issued2021-
dc.identifier.issn0923-6082-
dc.identifier.urihttp://digital.lib.ueh.edu.vn/handle/UEH/61914-
dc.description.abstractBreast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.en
dc.formatPortable Document Format (PDF)-
dc.language.isoeng-
dc.publisherSpringer-
dc.relation.ispartofMultidimensional Systems and Signal Processing-
dc.relation.ispartofseriesVol. 32-
dc.rightsThe Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.-
dc.subject3D mammographyen
dc.subjectBreast canceren
dc.subjectConvolutional neural networksen
dc.subjectHybrid transfer learningen
dc.subjectMammographyen
dc.subjectMedical image segmentationen
dc.titleDiagnosis of breast cancer based on modern mammography using hybrid transfer learningen
dc.typeJournal Articleen
dc.identifier.doihttps://doi.org/10.1007/s11045-020-00756-7-
dc.format.firstpage747-
dc.format.lastpage765-
ueh.JournalRankingScopus-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeJournal Article-
item.grantfulltextnone-
item.fulltextOnly abstracts-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:INTERNATIONAL PUBLICATIONS
Show simple item record

Google ScholarTM

Check

Altmetric


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