The morphological characteristics of bull spermatozoa are usually evaluated visually using bright-field microscopy
according to the guidelines proposed by the Society for Theriogenology (SFT) for the Bull Breeding
Soundness Evaluation (BBSE). However, analysis is labor consuming and requires experienced personnel to
obtain reliable results. Nevertheless, the artificial insemination industry increasingly demands the implementation
of genomic selection schemes for young bulls. Hence, there is a growing need for a more standardized
technique to analyze semen quality, particularly for the evaluation of spermatozoa abnormalities that affect
semen freezing suitability and fertilizing capacity. Therefore, an Artificial Intelligence (AI) algorithm for the
automated classification of microscope-acquired images of spermatozoa was developed using neural networks,
specifically YOLO networks, based on convolutional neural networks (CNNs) that were able to learn and extract
relevant features from complex visual data through image segmentation. The aim was to assess the algorithm
ability to identify sperm cells in microscope-acquired images, establish their viability and to classify morphology
based on a simplified scheme which included only normal or major/minor defect categories. The dataset
comprised 8243 images, which were labeled and annotated with bounding boxes to allow the segmentation
algorithm to learn. The performance obtained by the algorithm showed an accuracy of 82 %, although it was not
observed for all classes (excluding a probable case of overfitting where accuracy reached 100 %), and a precision
of 85 % in the correct classification of spermatozoa morphology. Results thereby confirmed the potential
applicability of the algorithm in bull semen analysis without excluding its future implementation for achieving
optimal performance.
