Efficient phenotypic sex classification of zebrafish using machine learning methods.

ABSTRACT: Sex determination in zebrafish by manual approaches according to current guidelines relies on human observation. These guidelines for sex recognition have proven to be subjective and highly labor-intensive. To address this problem, we present a methodology to automatically classify the phenotypic sex using two machine learning methods: Deep Convolutional Neural Networks (DCNNs) based on the whole fish appearance and Support Vector Machine (SVM) based on caudal fin coloration. Machine learning techniques in sex classification provide potential efficiency with the advantage of automatization and robustness in the prediction process. Furthermore, since developmental plasticity can be influenced by environmental conditions, we have investigated the impact of elevated water temperature during e

SUBMITTER: Hosseini S 

PROVIDER: S-EPMC6912926 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature