trish abnormal/normal Object Detection Dataset and Pre-Trained Model by trish

Images

2018 images

Explore Dataset

Here are a few use cases for this project:

Medical Diagnostics: The model can be used in hospitals, clinics, or doctor practices to help provide a quick preliminary diagnosis for patients, especially in urgent care settings. Identifying fractures and hairline fractures can expedite treatment and intervention plans.
Telemedicine App: The model can be integrated into telemedicine applications to provide virtual first-line diagnostics for patients who cannot readily access medical facilities. This is particularly useful in remote areas and for elderly or disabled people.
Emergency Rescue: The model can be used by rescue teams in disaster-stricken areas to quickly identify serious injuries that need immediate attention, allowing for more informed decision-making and prioritization of patients.
Training Medical Students: This model can be used to educate medical students in the process of identifying fractures and hairline fractures, providing valuable, hands-on training experience without the risks associated with real-world diagnostics.
Manufacturing and Industrial Safety: If similar models are developed for other types of X-rays, they can be used in situations involving workplace injuries in heavy industries. Quick diagnosis can help provide immediate necessary treatment and document the extent of injuries for workers' compensation claims.

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{
                            trish-abnormal-normal_dataset,
                            title = { trish abnormal/normal Dataset },
                            type = { Open Source Dataset },
                            author = { trish },
                            howpublished = { \url{ https://universe.roboflow.com/trish-zt1e7/trish-abnormal-normal } },
                            url = { https://universe.roboflow.com/trish-zt1e7/trish-abnormal-normal },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-04-27 },
                            }