A Fine-Tuned Convolution Neural Network Based Approach For Phenotype Classification Of Zebrafish Embryo

Abstract

In the area of medical imaging, automation of medical image classification or recognition is an active field of research. Over the past decade, due to the popularity and usability of artificial neural networks, it is becoming the norm to achieve this automation by deep neural networks. In toxicology based research, zebrafish has become a key model organism in phenotypical imaging and drug discovery. Detection of complex patterns and phenotypes in zebrafish embryo during preclinical or clinical trials is a standard part of the drug discovery cycle. Currently this classification task of phenotypes is mostly experts based. In this work, we propose a fine-tuned convolution neural network (CNN) based model for automated classification of different phenotypical changes observed due to the toxic substance in the zebrafish embryo. We demonstrated the ability of CNN model as well as a fine-tuned CNN based model to classify different deformation in an embryo with high accuracy. Such automated medical imaging model can be used extensively by experts in the area of toxicology and drug discovery.