A Preliminary Exploration into an Alternative CellLineNet: An Evolutionary Approach

• Main Authors: Nwachuku, Akwarandu Ugo, Lewis-Palmer, Xavier, Akogo, Darlington Ahiale
• Format: Journal Article
• Language: English
• Published: 25-07-2020
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.2007.13044

Within this paper, the exploration of an evolutionary approach to an
alternative CellLineNet: a convolutional neural network adept at the
classification of epithelial breast cancer cell lines, is presented. This
evolutionary algorithm introduces control variables that guide the search of
architectures in the search space of inverted residual blocks, bottleneck
blocks, residual blocks and a basic 2x2 convolutional block. The promise of
EvoCELL is predicting what combination or arrangement of the feature extracting
blocks that produce the best model architecture for a given task. Therein, the
performance of how the fittest model evolved after each generation is shown.
The final evolved model CellLineNet V2 classifies 5 types of epithelial breast
cell lines consisting of two human cancer lines, 2 normal immortalized lines,
and 1 immortalized mouse line (MDA-MB-468, MCF7, 10A, 12A and HC11). The
Multiclass Cell Line Classification Convolutional Neural Network extends our
earlier work on a Binary Breast Cancer Cell Line Classification model. This
paper presents an on-going exploratory approach to neural network architecture
design and is presented for further study.