A Comprehensive Image Dataset of Clinically Significant Ruminant Parasites in Bangladesh (Research in Agriculture Livestock and Fisheries, 2025)
This article presents a curated dataset comprising high-resolution images of five parasitic egg types commonly identified in fecal samples of ruminants in Bangladesh and the Indian subcontinent: Fasciola spp., Paramphistomum spp., Balantidium coli, Ascaris spp., and stomach worms belonging to the family Trichostrongylidae. The images were collected from a range of publicly available and academic sources, including peer-reviewed scientific journals, research articles, veterinary teaching hospital laboratories, and existing open-access datasets. All images were standardized to a uniform format and resolution, and each was annotated with the corresponding parasite genus to facilitate computer-based image analysis. The dataset also includes a set of negative control images that do not contain any parasitic structures. A consistent naming convention (ParasiteName_ImageNumber) and standardized file extensions were applied to ensure systematic organization and compatibility with automated processing pipelines. This dataset is intended for reuse in the development and evaluation of computer vision and artificial intelligence models for parasite identification in fecal samples, particularly for veterinary diagnostic applications. It offers a valuable resource for advancing image-based research in parasitology and has potential utility in diagnostic workflows, especially in resource-limited settings where conventional microscopy is unavailable. The dataset is openly accessible and designed to support a wide range of research and teaching activities in veterinary science, animal health, and computational biology.Res. Agric. Livest. Fish. Vol. 12, No. 3, December 2025: 493-499