Applications of Neural Networks for Classifying Images of Deaf Horses

Abstract

Equine deafness profoundly impacts the ways owners can interact with their horses; while deafness prevents many auditory distractions, it also requires trainers to communicate differently than with other horses. While the splashed-white genes (SW-1 through SW-5) have been known to express themselves in near-full facial coverage with white hair in addition to blue eyes, it regularly entails deafness in the host. Current diagnoses of the SW-5 gene are primarily limited to proprietary genome analysis provided by equine diagnostics companies such as Etalon Diagnostics, which requires both money and time. Our approach to diagnosing SW-5 leverages the present phenotype-genotype relationship to convert this diagnosis into an image classification task. We propose a technique that uses a convolutional neural network in order to classify SW-5 horses solely based on physical attributes. Our classifier predicted the SW-5 gene with 97.49% accuracy and 5.88% loss, which provides an instantaneous prediction within margins of confidence.