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.
@article{arxiv.1807.10638,
title = {End-to-End Learning via a Convolutional Neural Network for Cancer Cell Line Classification},
author = {Darlington Ahiale Akogo and Xavier-Lewis Palmer},
journal= {arXiv preprint arXiv:1807.10638},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1805.08702