Machine Learning-Based Individual Identification of Laboratory Mice

Abstract

Background and Objective: Individual identification of laboratory animals is essential in behavioral science. Conventional methods often involve invasive marking of the animals’ bodies and may cause infectious diseases. The purpose of this study was to establish an accurate and non-invasive method for the individual identification of native laboratory mice. Materials and Methods: A total of 706 photographs of four mice were taken. Using the photographs, an open-source computing algorithm method was adopted to create a model that identified individual mice. Results: Using the high-resolution photographs and the open-source computing algorithm with deep learning, an accurate algorithm for individual identification of mice, which outperformed classical identification algorithms, was established. Conclusion: Compared with the other conventional methods, this model exhibited higher performance in individual identification of mice. It will provide a platform to automatically label individual mice for social behavioral experiments.