Identification of Breast Cancer Metastasis Using Boosting Algorithms on Cytopathologic Data

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:

Breast cancer, eXtreme gradient boost, gradient boosting machine light gradient boosting machine,

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