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Computer Vision for Analyzing and Classifying cells and tissues often requirerigorous lab procedures and so automated Computer Vision solutions have beensought. Most work in such field usually requires Feature Extractions before theanalysis of such features via Machine Learning and Machine Vision algorithms.We developed a Convolutional Neural Network that classifies 5 types ofepithelial breast cell lines comprised of two human cancer lines, 2 normalimmortalized lines, and 1 immortalized mouse line (MDA-MB-468, MCF7, 10A, 12Aand HC11) without requiring feature extraction. The Multiclass Cell LineClassification Convolutional Neural Network extends our earlier work on aBinary Breast Cancer Cell Line Classification model. CellLineNet is 31-layerConvolutional Neural Network trained, validated and tested on a 3,252 imagedataset of 5 types of Epithelial Breast cell Lines (MDA-MB-468, MCF7, 10A, 12Aand HC11) in an end-to-end fashion. End-to-End Learning enables CellLineNet toidentify and learn on its own, visual features and regularities most importantto Breast Cancer Cell Line Classification from the dataset of images. UsingTransfer Learning, the 28-layer MobileNet Convolutional Neural Networkarchitecture with pre-trained ImageNet weights is extended and fine tuned tothe Multiclass Epithelial Breast cell Line Classification problem. CellLineNetsimply requires an imaged Cell Line as input and it outputs the type of breastepithelial cell line (MDA-MB-468, MCF7, 10A, 12A or HC11) as predictedprobabilities for the 5 classes. CellLineNet scored a 96.67% Accuracy.

CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network