SchiEsqui Object Detection Dataset and Pre-Trained Model by Schisto Alej

Images

4480 images

Explore Dataset

Here are a few use cases for this project:

Water Quality Monitoring: Utilizing the Schisto computer vision model to analyze water samples under a microscope, helping in the identification and estimation of worm populations, thus assisting in water quality assessment, and detecting potential threats to public health or aquatic ecosystems.
Parasitology Research: Assisting researchers and medical professionals who study schistosomiasis (a parasitic disease caused by Schistosoma worms) by automatically identifying and classifying different life stages of the parasites, streamlining the analysis process, and supporting their research.
Disease Surveillance and Control: Implementing the Schisto model in mobile laboratories or fieldwork to monitor for the presence and distribution of Schistosoma worms, enabling effective surveillance and rapid response in managing potential disease outbreaks or implementing control measures.
Education and Training: Using the Schisto computer vision model as a educational tool in universities, research institutions, or training programs, providing students and professionals with an innovative way to develop their skills in microscopy and parasite identification, while reinforcing their knowledge of Schistosoma worms and associated diseases.
Pharmaceutical and Treatment Development: Assisting pharmaceutical companies and medical professionals in the development of new pharmacological treatments or vaccines targeting Schistosoma worms, by providing rapid and accurate identification of parasite life stages, supporting high-throughput screening of potential drug candidates, and enabling efficient monitoring of treatment outcomes.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            schiesqui_dataset,
                            title = { SchiEsqui Dataset },
                            type = { Open Source Dataset },
                            author = { Schisto Alej },
                            howpublished = { \url{ https://universe.roboflow.com/schisto-alej/schiesqui } },
                            url = { https://universe.roboflow.com/schisto-alej/schiesqui },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { jun },
                            note = { visited on 2024-06-18 },
                            }