Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques

dc.contributor.authorBaccouche, Asma
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorZheng, Yufeng
dc.contributor.authorElmaghraby, Adel Said
dc.date.accessioned2024-11-14T11:37:30Z
dc.date.available2024-11-14T11:37:30Z
dc.date.issued2022-06
dc.date.updated2024-11-14T11:37:30Z
dc.description.abstractBackground and Objective: Computer-aided-detection (CAD) systems have been developed to assist radiologists on finding suspicious lesions in mammogram. Deep Learning technology have recently succeeded to increase the chance of recognizing abnormality at an early stage in order to avoid unnecessary biopsies and decrease the mortality rate. In this study, we investigated the effectiveness of an end-to-end fusion model based on You-Only-Look-Once (YOLO) architecture, to simultaneously detect and classify suspicious breast lesions on digital mammograms. Four categories of cases were included: Mass, Calcification, Architectural Distortions, and Normal from a private digital mammographic database including 413 cases. For all cases, Prior mammograms (typically scanned 1 year before) were all reported as Normal, while Current mammograms were diagnosed as cancerous (confirmed by biopsies) or healthy. Methods: We propose to apply the YOLO-based fusion model to the Current mammograms for breast lesions detection and classification. Then apply the same model retrospectively to synthetic mammograms for an early cancer prediction, where the synthetic mammograms were generated from the Prior mammograms by using the image-to-image translation models, CycleGAN and Pix2Pix. Results: Evaluation results showed that our methodology could significantly detect and classify breast lesions on Current mammograms with a highest rate of 93% ± 0.118 for Mass lesions, 88% ± 0.09 for Calcification lesions, and 95% ± 0.06 for Architectural Distortion lesions. In addition, we reported evaluation results on Prior mammograms with a highest rate of 36% ± 0.01 for Mass lesions, 14% ± 0.01 for Calcification lesions, and 50% ± 0.02 for Architectural Distortion lesions. Normal mammograms were accordingly classified with an accuracy rate of 92% ± 0.09 and 90% ± 0.06 respectively on Current and Prior exams. Conclusions: Our proposed framework was first developed to help detecting and identifying suspicious breast lesions in X-ray mammograms on their Current screening. The work was also suggested to reduce the temporal changes between pairs of Prior and follow-up screenings for early predicting the location and type of abnormalities in Prior mammogram screening. The paper presented a CAD method to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning and image-to-image translation for a biomedical application.en
dc.identifier.citationBaccouche, A., Garcia-Zapirain, B., Zheng, Y., & Elmaghraby, A. S. (2022). Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques. Computer Methods and Programs in Biomedicine, 221. https://doi.org/10.1016/J.CMPB.2022.106884
dc.identifier.doi10.1016/J.CMPB.2022.106884
dc.identifier.eissn1872-7565
dc.identifier.issn0169-2607
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1859
dc.language.isoeng
dc.publisherElsevier Ireland Ltd
dc.rights© 2022 The Author(s)
dc.subject.otherBreast cancer
dc.subject.otherClassification
dc.subject.otherDetection
dc.subject.otherEarly diagnosis
dc.subject.otherPrior mammogram
dc.subject.otherYOLO
dc.titleEarly detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniquesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleComputer Methods and Programs in Biomedicine
oaire.citation.volume221
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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