Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification
Yazarlar (3)
Prof. Dr. Kemal Adem Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Doç. Dr. Serhat Kılıçarslan Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Dr. Öğr. Üyesi Onur CÖMERT Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | EXPERT SYSTEMS WITH APPLICATIONS (Q1) | ||
| Dergi ISSN | 0957-4174 Dergi Bilgileri (2019) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2019 |
| Cilt / Sayı / Sayfa | 115 / 1 / 557–564 | DOI | 10.1016/j.eswa.2018.08.050 |
| Makale Linki | https://www.sciencedirect.com/science/article/pii/S0957417418305645 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| Cervical cancer is one of the most common cancer types in the world, which causes many people to lose their lives. Cancer research is of importance since early diagnosis of cancer facilitates clinical applications for patients. The aim of the study is the automatic diagnosis of cervical cancer. For this purpose, a data set containing 668 samples, 30 attributes and 4 target variables (Schiller, Citology, Biopsy and Hinselmann) from the UCI database was used in the training and testing phases. Softmax classification with stacked autoencoder, one of the deep learning methods, has been used to classify data sets. At first, by applying stacked autoencoder to the raw data set, a reduced dimension dataset is obtained. This data set has been trained for classification by applying the softmax layer. In this phase, 70% (468) of the data set was allocated for training and the remaining 30% (200) for testing. In order to compare the … |
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Science Direct
Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Yöksis | 93 |
| Web of Science | 132 |
| Scopus | 180 |
| Google Scholar | 228 |