Sperm morphology is crucial in semen analysis for diagnosing male infertility. To reduce limitations in visual assessment, such as variability in biological conditions and the biologist’s experience, developing computer-based sperm analysis techniques is imperative. In this study, a total of 49345 RGB sperm morphology patches were obtained using the proposed image acquisition technique and three different Diff-Quick staining methods: BesLab, Histoplus, and GBL. The images were labeled by experts under 18 classes, including sperm head, neck, and tail abnormality types, along with a normal class. The head category includes amorphous, tapered, double, pyriform, pin, vacuolated, narrow acrosome, and round. The neck category encompasses thin, thick, twisted, and asymmetrical. The tail category includes double, curly, long, short, and twisted. The Efficient-V2-Medium achieved accuracy rates of 65.05% and 67.42% on the BesLab and Histoplus datasets, respectively, while the GBL dataset yielded an accuracy of 63.58% using the Efficient-V2-Small. This study experimentally demonstrates that the Histoplus staining method is more suitable for deep learning-based automated analysis systems. As a reference for future studies, 35 different deep learning architectures were trained on the proposed dataset, establishing a classification baseline. The results show that the dataset can be successfully applied to complex deep learning models. Additionally, it addresses the absence of a large-scale sperm morpholgy analysis public datasets and can serve as a standard benchmark for future studies.