Automatic breast cancer cell classification using deep convolutional neural networks
Gisela Pattarone · Journal of Science Humanities and Arts - JOSHA · 2020
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.josha.