End-to-End Learning via a Convolutional Neural Network for Cancer Cell Line Classification

TL;DR

This paper presents a deep learning model that classifies breast cancer cell lines from microscopy images with high accuracy, eliminating the need for manual feature extraction.

Contribution

The authors developed a 6-layer CNN that directly classifies cancer cell lines from images in an end-to-end manner, achieving 99% accuracy.

Findings

Achieved 99% classification accuracy.

Eliminated manual feature extraction step.

Validated on 1,241 images.

Abstract

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.