Breast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methods

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874en_US
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017en_US
dc.contributor.authorRanjbarzadeh, Ramin
dc.contributor.authorDorosti, Shadi
dc.contributor.authorJafarzadeh Ghoushchi, Saeid
dc.contributor.authorCaputo, Annalina
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorAli, Sadia Samar
dc.contributor.authorArshadi, Zahra
dc.contributor.authorBendechache, Malika
dc.date.accessioned2023-02-03T07:40:11Z
dc.date.available2023-02-03T07:40:11Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThe Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case. © 2022 Elsevier Ltden_US
dc.identifier.citationRanjbarzadeh, R., Dorosti, S., Ghoushchi, S. J., Caputo, A., Tirkolaee, E. B., Ali, S. S., ... & Bendechache, M. (2022). Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods. Computers in Biology and Medicine, 106443.en_US
dc.identifier.doi10.1016/j.compbiomed.2022.106443en_US
dc.identifier.issn0010-4825en_US
dc.identifier.scopus2-s2.0-85144580196en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.compbiomed.2022.106443
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3864
dc.identifier.volume152en_US
dc.identifier.wosWOS:000909807600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast Canceren_US
dc.subjectDeep Learningen_US
dc.subjectImage Segmentationen_US
dc.subjectTumor Segmentationen_US
dc.titleBreast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methodsen_US
dc.typeOtheren_US

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