Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data

Safak Kayıkcı

Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data

Öz

Breast cancer is the second most common cancer among women after lung cancer. Early diagnosis of cancer can positively affect the recovery process from disease. Several machine learning-based approaches have been studied for cancer detection on histopathological images. In this study, identification of cancer type has been made using Gradient Boosting Machine (GBM), eXtreme Gradient Boost (XGBoost), and Light Gradient Boosting Machine (LightGBM) algorithms. The performances of these techniques have been measured on the Breast Cancer Wisconsin (Diagnostic) dataset. According to the results obtained, Gradient Boosting Machine (GBM) got the highest accuracy rate with 97.02% success. Although there is no pathological prior knowledge about the disease, high success has been achieved in diagnosing with the deep learning architectures used.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Zeka

Bölüm

Araştırma Makalesi

Yazarlar

Safak Kayıkcı ^* Bu kişi benim
Türkiye

Yayımlanma Tarihi

30 Ağustos 2021

Gönderilme Tarihi

29 Haziran 2021

Kabul Tarihi

17 Ağustos 2021

Yayımlandığı Sayı

Yıl 2021 Cilt: 1 Sayı: 1

IZ

https://izlik.org/JA28FW87GF

Kaynak Göster

RIS / Bibtex

APA

Kayıkcı, S. (2021). Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data. Journal of Artificial Intelligence and Data Science, 1(1), 11-21. https://izlik.org/JA28FW87GF

AMA

1.Kayıkcı S. Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data. Journal of Artificial Intelligence and Data Science. 2021;1(1):11-21. https://izlik.org/JA28FW87GF

Chicago

Kayıkcı, Safak. 2021. “Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data”. Journal of Artificial Intelligence and Data Science 1 (1): 11-21. https://izlik.org/JA28FW87GF.

EndNote

Kayıkcı S (01 Ağustos 2021) Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data. Journal of Artificial Intelligence and Data Science 1 1 11–21.

IEEE

[1]S. Kayıkcı, “Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data”, Journal of Artificial Intelligence and Data Science, c. 1, sy 1, ss. 11–21, Ağu. 2021, [çevrimiçi]. Erişim adresi: https://izlik.org/JA28FW87GF

ISNAD

Kayıkcı, Safak. “Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data”. Journal of Artificial Intelligence and Data Science 1/1 (01 Ağustos 2021): 11-21. https://izlik.org/JA28FW87GF.

JAMA

1.Kayıkcı S. Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data. Journal of Artificial Intelligence and Data Science. 2021;1:11–21.

MLA

Kayıkcı, Safak. “Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data”. Journal of Artificial Intelligence and Data Science, c. 1, sy 1, Ağustos 2021, ss. 11-21, https://izlik.org/JA28FW87GF.

Vancouver

1.Safak Kayıkcı. Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data. Journal of Artificial Intelligence and Data Science [Internet]. 01 Ağustos 2021;1(1):11-2. Erişim adresi: https://izlik.org/JA28FW87GF