Malaria Parasite Object Detection Dataset and Pre-Trained Model by None

Images

1000 images

Explore Dataset

Here are a few use cases for this project:

Medical Diagnostics: The "Malaria Parasite" computer vision model can be utilized by diagnostic labs to analyze patients' blood samples, making it easier and faster to identify the presence and class of malaria parasites.
Remote Healthcare: This model can be used in telemedicine platforms to allow healthcare practitioners in remote areas to identify malaria parasites from blood sample images, improving the speed and accessibility of malaria detection in underserved areas.
Research and Development: Scientists and researchers studying malaria can make use of this model to quickly identify and differentiate between parasite classes, accelerating their research process and facilitating the creation of new treatments or vaccines.
Educational Tool: The model can be integrated into digital learning platforms to visually teach medical students or healthcare workers about malaria parasites, helping them better understand different types of parasites and how to identify them.
Public Health Monitor: Public health organizations could use this model to monitor and assess the prevalence of different types of malaria parasites in various regions, contributing to more efficient disease tracking and potentially influencing public health policies.

(Note: The mentioned "pink and white pixelated image of a bird" doesn't seem to be relevant to the "Malaria Parasite" computer vision model and might have been included by error. The model specifically deals with malaria parasites, which would involve images of blood cells rather than birds.)

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{
                            malaria-parasite-kxdeb_dataset,
                            title = { Malaria Parasite Dataset },
                            type = { Open Source Dataset },
                            author = { None },
                            howpublished = { \url{ https://universe.roboflow.com/none-pmavh/malaria-parasite-kxdeb } },
                            url = { https://universe.roboflow.com/none-pmavh/malaria-parasite-kxdeb },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { sep },
                            note = { visited on 2024-05-07 },
                            }