Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data

Safak Kayıkcı

Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data

Abstract

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.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence

Journal Section

Research Article

Authors

Safak Kayıkcı ^* This is me
Türkiye

Publication Date

August 30, 2021

Submission Date

June 29, 2021

Acceptance Date

August 17, 2021

Published in Issue

Year 2021 Volume: 1 Number: 1

IZ

https://izlik.org/JA28FW87GF

Cite

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 (August 1, 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, vol. 1, no. 1, pp. 11–21, Aug. 2021, [Online]. Available: 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 (August 1, 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, vol. 1, no. 1, Aug. 2021, pp. 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]. 2021 Aug. 1;1(1):11-2. Available from: https://izlik.org/JA28FW87GF