  Computer Vision for automated analysis of cells and tissues usually include
extracting features from images before analyzing such features via various
Machine Learning and Machine Vision algorithms. We developed a Convolutional
Neural Network model that classifies MDA-MB-468 and MCF7 breast cancer cells
via brightfield microscopy images without the need of any prior feature
extraction. Our 6-layer Convolutional Neural Network is directly trained,
validated and tested on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell
line in an end-to-end fashion, allowing a system to distinguish between
different cancer cell types. The model takes in as input imaged breast cancer
cell line and then outputs the cell line type (MDA-MB-468 or MCF7) as predicted
probabilities between the two classes. Our model scored a 99% Accuracy.
