ABSTRACT
Hormone receptor status in breast carcinoma is determined primarily to identify patients who may benefit from hormonal therapy. Estrogen receptor (ER) is one of the hormone receptor positive factors which have been recognized as a marker for which women with breast cancer would respond to hormone treatment. We propose a system to classify cells in ER-stained whole slide breast carcinoma images according to their staining strength using convolutional neural network (CNN). The proposed CNN multiclass classifier was tested on a region of 1200 cells, and achieved very promising results, with overall accuracy of 88.8% and AUC score of 97.5%. The proposed system is useful for use in hormone receptor testing, where the outcomes are used to decide whether the cancer is likely to respond to hormonal therapy or other treatments.
