Automatic Breast Cancer Cell Classification using deep Convolutional neural Networks
Affiliation: Faculty of Pharmacy and Biochemistry, Faculty of Medicine – Universidad de Buenos Aires, Argentina and Faculty of Medicine – Albert Ludwigs University of Freiburg, Germany
Keywords: breast cancer cell, medicine Program, Argentina, Germany, biomedical Sciences
Categories: Medicine, Artificial Intelligence, Modeling and Simulation
Automated cell classification in cancer biology is an active and challenging task for computer vision and machine learning algorithms. In this Thesis, we first compiled a vast data set composed of JIMT-1 human breast cancer cell line images, with and without therapeutic drug treatment. We then train a Convolutional Neural Network architecture to perform classification using per-cell labels obtained from fluorescence microscopy images associated with each brightfield image. The study revealed that our classification model achieves 65% accuracy in breast cancer cells under chemotherapeutic drug treatment with doxorubicin and paclitaxel. Furthermore, it reached 70% accuracy when classifying breast cancer cells without drug treatment. Our results highlight the potential of machine learning and image analysis algorithms to build new diagnosis tools.