Categorization of breast carcinoma histopathology ımages by utilizing region-based convolutional neural networks

Basit Öğe Kaydı
dc.contributor.authorAltuntaş, Tuğçe Sena
dc.contributor.authorToyran, Tuğba
dc.contributor.authorArıca, Sami
dc.date.accessioned2024-06-13T20:17:52Z
dc.date.available2024-06-13T20:17:52Z
dc.date.issued2024
dc.departmentMeslek Yüksekokulu, Gedik Meslek Yüksekokulu, Elektrik Programı
dc.description.abstractThe inadequacy of experienced pathologists worldwide, combined with the workload of current specialists, has increased the need for digital pathology. Accordingly, in this study, the categorization of breast cancer histopathology images supplied by ICIAR 2018 was carried out using region-based convolutional neural networks (R-CNN) based on transfer learning with custom augmentation, training parameters and patch size selection. The images were normalized using a stain normalization method to reduce inequalities in color distribution. Image patches were extracted, and a transfer learning technique was performed to solve the lack of data. ResNet-18 was utilized for transfer learning. Image augmentation was also performed to increase the training data. The network achieved a test accuracy of 93.75% and 97.06% for four classes and two classes, respectively, on the training dataset. The success of our method was also examined on the blind test set, and it had 73.44% accuracy for four classes and 87.24% accuracy for two classes in patch-wise classification, while it obtained 69.79% accuracy for four classes and 86.46% accuracy for two classes in image-wise classification. Our model achieved a score very close to the highest result in the literature, with a difference of 1.51% for two classes and 4.36% for four classes in patch-wise classification. The outcomes show that R-CNN with transfer learning gives competitive results with state-of-the-art studies in the literature in this dataset and can be used as a tool to aid pathologists.
dc.description.sponsorshipScientific Research ProjectUnit of Cukurova University [FDK-2019-11505]
dc.identifier.doi10.1007/s13369-023-08387-3
dc.identifier.endpage6705
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85175532082
dc.identifier.scopusqualityQ1
dc.identifier.startpage6695
dc.identifier.urihttps://doi.org/10.1007/s13369-023-08387-3
dc.identifier.urihttps://hdl.handle.net/11501/1123
dc.identifier.volume49
dc.identifier.wosWOS:001093287600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAltuntaş, Tuğçe Sena
dc.institutionauthorid0000-0003-2083-251X
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal for Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBreast Cancer Classification
dc.subjectComputer-Aided Diagnosis
dc.subjectDeep Learning
dc.subjectDigital Pathology
dc.titleCategorization of breast carcinoma histopathology ımages by utilizing region-based convolutional neural networks
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Tam Metin / Full Text
Boyut:
959.02 KB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Gedik Meslek Yüksekokulu Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu