Parasitic Egg Image Database
Description
This dataset is a curated collection of images showing parasitic eggs found in ruminant fecal samples, focusing on five commonly observed species in Bangladesh and the Indian subcontinent: Fasciola spp., Paramphistomum spp., Balantidium coli, Ascaris spp., and stomach worms from the Trichostrongiloidae family. The images were captured using light microscopy following standardized procedures to ensure consistent clarity and quality. Negative control images come from samples without visible parasites, while additional images were sourced from peer-reviewed journals and reliable online platforms. Each parasite type has its own folder containing 50 labeled images that highlight key morphological features essential for accurate identification and classification. Alongside the 250 parasitic egg images, the dataset includes 300 negative control images. All images are available in JPG, JPEG, and PNG formats. Each folder also contains an Excel file with detailed information about the source, collection context, and sample metadata. This dataset was developed to address the lack of organized and accessible image repositories of livestock parasites in South Asia, particularly in Bangladesh where parasitic infestations in ruminants are common. Traditional diagnostic techniques rely heavily on microscopy, which requires expertise and is often time-consuming. By providing a publicly accessible and well-structured visual reference, this dataset aims to improve the efficiency of parasitological diagnosis and training. It is also intended to support researchers in machine learning and computer vision by offering distinct shapes, shell structures, and internal features of parasite eggs that are valuable for automated classification and morphological studies. This resource is designed to assist veterinary parasitologists, epidemiologists, researchers, educators, and students. It encourages reuse for academic training, comparative research, model development, and educational purposes. Users are invited to cite this dataset in relevant work and refer to the accompanying README file for comprehensive guidance on image formats, metadata, and data organization.
Files
Institutions
- Bangladesh Agricultural University