Breast lesions detection and classification via YOLO-based fusion models

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dc.contributor.authorBaccouche, Asma
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorCastillo Olea, Cristian
dc.contributor.authorElmaghraby, Adel Said
dc.date.accessioned2025-08-12T08:29:36Z
dc.date.available2025-08-12T08:29:36Z
dc.date.issued2021-06-04
dc.date.updated2025-08-12T08:29:36Z
dc.description.abstractWith recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images, then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification. We evaluated the model on two publicly available datasets, with 2907 mammograms from the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and 235 mammograms from INbreast database. We also used a privately collected dataset with 487 mammograms. Furthermore, we suggested a fusion models approach to report more precise detection and accurate classification. Our best results reached a detection accuracy rate of 95.7%, 98.1% and 98% for mass lesions and 74.4%, 71.8% and 73.2% for calcification lesions, respectively on CBIS-DDSM, INbreast and the private dataset.en
dc.identifier.citationBaccouche, A., Garcia-Zapirain, B., Olea, C. C., & Elmaghraby, A. S. (2021). Breast lesions detection and classification via YOLO-based fusion models. Computers, Materials and Continua, 69(1), 1407-1425. https://doi.org/10.32604/CMC.2021.018461
dc.identifier.doi10.32604/CMC.2021.018461
dc.identifier.eissn1546-2226
dc.identifier.issn1546-2218
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3353
dc.language.isoeng
dc.publisherTech Science Press
dc.rights© 2021 The Author(s)
dc.subject.otherBreast cancer
dc.subject.otherClassification
dc.subject.otherDeep learning
dc.subject.otherDetection
dc.subject.otherFusion
dc.subject.otherYOLO
dc.titleBreast lesions detection and classification via YOLO-based fusion modelsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage1425
oaire.citation.issue1
oaire.citation.startPage1407
oaire.citation.titleComputers, Materials and Continua
oaire.citation.volume69
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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