Alterations of the nanoscale structure of live HeLa cells after chemical fixation, as observed using partial wave spectroscopic optical microscopy. (Image: Vadim Backman, Northwestern University)
Classification of Breast Cancer Tumors Using Machine Learning
Breast cancer is the second leading cause of cancer deaths among women in the US. The frequency of breast cancer cases has increased steadily; however, the mortality rates have been stable due to advances in the early detection and treatment of breast cancer. Early detection of breast cancer is key to a good prognosis, and this is evident by the widespread adoption of traditional screening methods such as mammography, biopsy, and MRI. Conventional screening methods require interpreting a set of specialized signals by human observers. Machine learning algorithms can detect such cancerous signals from data and indicate the presence of malignancy. As breast cancer cases rise, well-designed machine learning models can be implemented to handle large volumes of data and optimized to reduce false positive and false negative rates. In this study, we demonstrate the use of machine learning in classifying breast cancer tumors from the Wisconsin Breast Cancer Dataset.
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