A novel deep feature extraction engineering for subtypes of breast cancer diagnosis: a transfer learning approach

Basit Öğe Kaydı
dc.authorid0000-0003-1606-4079en_US
dc.contributor.authorMuhammad, Bilyaminu
dc.contributor.authorÖzkaynak, Fatih
dc.contributor.authorVarol, Asaf
dc.contributor.authorTuncer, Türker
dc.date.accessioned2024-07-12T20:58:07Z
dc.date.available2024-07-12T20:58:07Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractFeature extraction from histological images is a challenging part of computer-aided detection of breast cancer. For this research, we present a novel technique for deep feature extraction for breast cancer diagnosis subtypes based on a transfer learning approach using the BreaKhis dataset. This approach consists of five phases: feature extraction, concatenation, transformation, selection, and classification. In the first phase, nineteen pre-trained convolutional neural networks were used as feature extractors to extract features from the input images. A Support Vector Machine was used at the feature extraction phase to calculate the misclassification rate of each feature generated by the pre-trained networks used. The feature extraction results showed that the two networks achieved the highest accuracy on the dataset and outperformed the other networks. The two networks considered were selected and connected to create the DRNet model, combining the pretrained networks ResNet50 and DenseNet201. The extracted features were decomposed into five sub-hand low-level features using a multilevel discrete wavelet transform in the transformation phase. An iterative neighborhood component analyzer was used to select the minimum number of features needed in the classification phase. A cubic support vector machine was used as a classifier in the final phase. Average classification accuracy of 98.61%, 98.04%, 97.68%, and 97.71% for the 40×, 100×, 200×, and 400× magnification levels, respectively, was achieved.en_US
dc.identifier.citationMuhammad, B., Özkaynak, F., Varol, A. and Tuncer, T. (2022). A novel deep feature extraction engineering for subtypes of breast cancer diagnosis: a transfer learning approach. 10th International Symposium on Digital Forensics and Security (ISDFS), p.1-7.en_US
dc.identifier.endpage7en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12415/3142
dc.institutionauthorva, Asaf
dc.language.isoenen_US
dc.relation.ispartof10th International Symposium on Digital Forensics and Security (ISDFS)en_US
dc.relation.isversionof10.1109/ISDFS55398.2022.9800813en_US
dc.relation.publicationcategoryUlusal Konferans Öğesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKY06088
dc.subjectFeature extractionen_US
dc.subjectHistological imagesen_US
dc.subjectPretrained networksen_US
dc.subjectTransfer learningen_US
dc.titleA novel deep feature extraction engineering for subtypes of breast cancer diagnosis: a transfer learning approachen_US
dc.typeConference Object
dspace.entity.typePublication

Dosyalar

Koleksiyon

Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu