An extended approach to the diagnosis of tumour location in breast cancer using deep learning

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorGhoushchi, Saeid Jafarzadeh
dc.contributor.authorRanjbarzadeh, Ramin
dc.contributor.authorNajafabadi, Saeed Aghasoleimani
dc.contributor.authorOsgooei, Elnaz
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2021-12-07T05:27:59Z
dc.date.available2021-12-07T05:27:59Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractBreast cancer is one of the most common and deadly cancers in women. However, early detection increases the likelihood of survival by 100%. Radiologists use mammograms to take X-ray images of the breast to look for signs of tumour formation, such as breast masses. The purpose of detecting these signs using convolutional networks is a modern machine learning (ML) model that performs image segmentation in one learning step. Therefore, this study develops a new machine learning approach based on modified deep learning (DL) to diagnose the tumour location in breast cancer. In this study, the data obtained from the databases (BCDRD01) are developed and resized and divided into data sets. A simple architecture is used for the first group of experiments, one of which utilizes a weighted function to counter the class imbalance. At first, after visualizing the images and using the Gabor filter, the exact location of the breast tissue is determined. In the following, two other complicated network-based architectures (VGG) (9 layers and 2.9 million parameters) and remaining networks (10 layers and 0.9 million parameters) are employed for the following experiments. The results indicate that convolutional neural networks (CNNs) are an appropriate option for the separation of breast cancer lesions.en_US
dc.identifier.citationJafarzadeh Ghoushchi, S., Ranjbarzadeh, R., Najafabadi, S. A., Osgooei, E., & Tirkolaee, E. B. (2021). An extended approach to the diagnosis of tumour location in breast cancer using deep learning. Journal of Ambient Intelligence and Humanized Computing, 1-11.en_US
dc.identifier.doi10.1007/s12652-021-03613-yen_US
dc.identifier.issn1868-5137en_US
dc.identifier.issn1868-5145en_US
dc.identifier.scopus2-s2.0-85120350518en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s12652-021-03613-y
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2307
dc.identifier.wosWOS:000723991400003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherSPRINGER HEIDELBERGen_US
dc.relation.ispartofJOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectDeep Learningen_US
dc.subjectDiagnosisen_US
dc.subjectBreast Canceren_US
dc.titleAn extended approach to the diagnosis of tumour location in breast cancer using deep learningen_US
dc.typeArticleen_US

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