ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition

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dc.authoridJafarzadeh-Ghoushchi, Saeid/0000-0003-3665-9010
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridAli, Sadia Samar/0000-0003-4911-5725
dc.authorwosidJafarzadeh-Ghoushchi, Saeid/AAC-7253-2019
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.authorwosidAli, Sadia Samar/B-6171-2019
dc.contributor.authorRanjbarzadeh, Ramin
dc.contributor.authorJafarzadeh Ghoushchi, Saeid
dc.contributor.authorTataei Sarshar, Nazanin
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorAli, Sadia Samar
dc.contributor.authorKumar, Teerath
dc.contributor.authorBendechache, Malika
dc.date.accessioned2024-05-19T14:39:38Z
dc.date.available2024-05-19T14:39:38Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractBreast tumor segmentation and recognition from mammograms play a key role in healthcare and treatment services. As different tumors in mammography have dissimilar densities, shapes, sizes, and edges, the interpretation of mammograms can be time-consuming and prone to interpretation variability even for a highly trained radiologist or expert. In this study, several encoding approaches are first proposed to achieve an effective breast cancer recognition system as well as create new images from the input image. Each encoded image represents some unique features that are crucial for detecting the target texture properly. Subsequently, pectoral muscle is eliminated using obtained features from these encoded images. Moreover, 11 distinct images are then applied to a shallow and efficient cascade Convolutional Neural Network (CNN) for classifying each pixel inside the image. This network accepts 11 local patches as the input from 11 obtained encoded images. Next, all extracted features are concatenated to a vertical vector to apply to the fully connected layers. Using different representations of the input mammogram images, the suggested model is able to analyze the input texture more effectively without using a deep CNN model. Finally, comprehensive experiments are then conducted on two public datasets which then demonstrate that the proposed framework successfully is able to gain competitive outcomes compared to a number of baselines.en_US
dc.description.sponsorshipScience Foundation Ireland [18/CRT/6183]; ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme [13/RC/2106/_P2]; Lero SFI Centre for Software [13/RC/2094/_P2]; European Regional Development Funden_US
dc.description.sponsorshipThis publication has emanated from research [conducted with the financial support of/supported in part by a grant from Science Foundation Ireland under Grant number No. 18/CRT/6183 and is supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106/_P2), Lero SFI Centre for Software (Grant 13/RC/2094/_P2) and is co-funded under the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.en_US
dc.identifier.doi10.1007/s10462-023-10426-2
dc.identifier.endpage10136en_US
dc.identifier.issn0269-2821
dc.identifier.issn1573-7462
dc.identifier.issue9en_US
dc.identifier.pmid38083110en_US
dc.identifier.scopus2-s2.0-85148355205en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage10099en_US
dc.identifier.urihttps://doi.org10.1007/s10462-023-10426-2
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4820
dc.identifier.volume56en_US
dc.identifier.wosWOS:000934858400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofArtificial Intelligence Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBreast Canceren_US
dc.subjectTumor Segmentationen_US
dc.subjectMedical Image Analysisen_US
dc.subjectDeep Learningen_US
dc.titleME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognitionen_US
dc.typeArticleen_US

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