Classifying mitotic cells based on texture features using AdaBoost : A case of highly imbalanced data

Counting mitotic figures present in tissue samples from a patient with cancer, plays a crucial role in assessing the breast cancer patient s survival chances. However, detecting mitoses under a microscope is a laborious, time-consuming task which can benefit from computer aided diagnosis. In this research we aim to detect mitotic cells present in breast cancer tissue, using only texture and pattern features. To classify cells into mitotic and non-mitotic classes, we use an AdaBoost classifier, an ensemble learning method which uses other (weak) classifiers to construct a strong classifier. 11 different classifiers were used separately as base learners, and their classification performance was recorded. It was observed that an AdaBoost that used Logistic Regression as its base learner achieved F1-Score of 0.85 using only texture features as input which shows a significant performance improvement over the status quo.

Keywords:

Mitosis detection , breast cancer , computer aided diagnosis , texture features , ensemble learning