Sperm morphology analysis is critical for assessing bovine fertility, since it provides insight into bull reproductive potential as well as subfertility and infertility. Traditional sperm morphology analysis is time-consuming, subjective, and prone to human error, all of which highlight the need for automated, objective solutions. This study presents the design and implementation of a computer-aided system for bovine sperm morphology analysis, leveraging deep learning models to detect and classify sperm cells based on their morphological characteristics. Using micrographs of bull sperm, we present a sequential deep learning framework that automatically detects morphological sperm aberrations. The model segments and analyzes each cell, identifying defects in the head, neck/midpiece, tail, and residual cytoplasm. Specifically, the system employs the YOLOv7 object detection framework, trained on a dataset of 277 annotated images comprising six morphological categories, to automatically identify and classify sperm abnormalities. The experimental results demonstrate a global mAP@50 of 0.73, precision of 0.75, and recall of 0.71, indicating a balanced tradeoff between accuracy and efficiency. By reducing reliance on manual analysis, this work enhances efficiency and accuracy in animal reproduction laboratories, contributing to veterinary reproduction through a cost-effective and scalable solution for sperm quality assessment.