A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model
Yazarlar (3)
Dr. Öğr. Üyesi Kadir Can BURÇAK Kırşehir Ahi Evran Üniversitesi, Türkiye
Harun Uğuz Konya Teknik Üniversitesi, Türkiye
Ömer Kaan Baykan Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | JOURNAL OF SUPERCOMPUTING (Q2) | ||
| Dergi ISSN | 0920-8542 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2021 |
| Cilt / Sayı / Sayfa | 77 / 1 / 973–989 | DOI | 10.1007/s11227-020-03321-y |
| Makale Linki | http://dx.doi.org/10.1007/s11227-020-03321-y | ||
| Özet |
| Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields such as drug discovery, time-series modelling and optimisation methods. With regard to the analysis of histopathologic breast cancer images, the similarity of those images and the presence of healthy and tumourous tissues in different areas complicate the detection and classification of tumours on whole slide images. An accurate diagnosis in a short time is a need for full treatment in breast cancer. A successful classification on breast cancer histopathological images will overcome the burden on the pathologist and reduce the subjectivity of diagnosis. In this study, we propose a deep convolutional neural network model. The model uses various algorithms (i.e., stochastic gradient descent, Nesterov accelerated gradient, adaptive gradient, RMSprop … |
| Anahtar Kelimeler |
| Deep learning | Convolutional neural network | Breast cancer | Histopathology | Image classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 47 |
| Google Scholar | 81 |