Detection of breast cancer via deep convolution neural networks using MRI images

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dc.contributor.authorYurttakal, Ahmet Hasim
dc.contributor.authorErbay, Hasan
dc.contributor.authorIkizceli, Turkan
dc.contributor.authorKaracavus, Seyhan
dc.date.accessioned2021-01-14T18:10:35Z
dc.date.available2021-01-14T18:10:35Z
dc.date.issued2020
dc.departmentKKÜ
dc.descriptionIkizceli, Turkan/0000-0002-5683-0391; Erbay, Hasan/0000-0002-7555-541X; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466
dc.description.abstractBreast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655.en_US
dc.identifier.citationBu makale açık erişimli değildir.en_US
dc.identifier.doi10.1007/s11042-019-7479-6
dc.identifier.endpage15573en_US
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.issue21-22en_US
dc.identifier.scopus2-s2.0-85074881501
dc.identifier.scopusqualityQ1
dc.identifier.startpage15555en_US
dc.identifier.urihttps://doi.org/10.1007/s11042-019-7479-6
dc.identifier.urihttps://hdl.handle.net/20.500.12587/12687
dc.identifier.volume79en_US
dc.identifier.wosWOS:000538675900065
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSPRINGERen_US
dc.relation.ispartofMULTIMEDIA TOOLS AND APPLICATIONS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBreast canceren_US
dc.subjectConvolutional neural networken_US
dc.subjectClassificationen_US
dc.titleDetection of breast cancer via deep convolution neural networks using MRI imagesen_US
dc.typeArticle

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