  Computer Vision for Analyzing and Classifying cells and tissues often require
rigorous lab procedures and so automated Computer Vision solutions have been
sought. Most work in such field usually requires Feature Extractions before the
analysis of such features via Machine Learning and Machine Vision algorithms.
We developed a Convolutional Neural Network that classifies 5 types of
epithelial breast cell lines comprised of two human cancer lines, 2 normal
immortalized lines, and 1 immortalized mouse line (MDA-MB-468, MCF7, 10A, 12A
and HC11) without requiring feature extraction. The Multiclass Cell Line
Classification Convolutional Neural Network extends our earlier work on a
Binary Breast Cancer Cell Line Classification model. CellLineNet is 31-layer
Convolutional Neural Network trained, validated and tested on a 3,252 image
dataset of 5 types of Epithelial Breast cell Lines (MDA-MB-468, MCF7, 10A, 12A
and HC11) in an end-to-end fashion. End-to-End Learning enables CellLineNet to
identify and learn on its own, visual features and regularities most important
to Breast Cancer Cell Line Classification from the dataset of images. Using
Transfer Learning, the 28-layer MobileNet Convolutional Neural Network
architecture with pre-trained ImageNet weights is extended and fine tuned to
the Multiclass Epithelial Breast cell Line Classification problem. CellLineNet
simply requires an imaged Cell Line as input and it outputs the type of breast
epithelial cell line (MDA-MB-468, MCF7, 10A, 12A or HC11) as predicted
probabilities for the 5 classes. CellLineNet scored a 96.67% Accuracy.
