Tuberculosis Semantic Segmentation Dataset and Pre-Trained Model by Fracture Detection

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Explore Dataset

Here are a few use cases for this project:

Medical Diagnosis: The model can be deployed in clinics and hospitals to assist clinicians in identifying Tuberculosis in patients during preliminary screenings. It can help speed up diagnosis, reducing the burden on medical staff.
Remote Healthcare: It's particularly useful in remote areas where expert medical professionals may not be readily available. Healthcare providers can use this model to analyze chest X-rays and provide initial TB diagnosis.
Telemedicine Services: Online healthcare and telemedicine services can use the model to provide quick and efficient preliminary diagnosis to their users worldwide. Users can take chest X-rays locally and upload them to the platform for analysis.
Training Medical Students: The model can act as a teaching aid for educating medical students on how to identify Tuberculosis in lungs. It can enhance practical learning by providing real-time feedback on their diagnosis.
Pharmaceutical Research: Drug development companies and researchers can use the model when testing new TB treatments. They can quickly identify TB presence in the lungs and monitor how it changes in response to medication.

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{
                            tuberculosis-apcje_dataset,
                            title = { Tuberculosis Dataset },
                            type = { Open Source Dataset },
                            author = { Fracture Detection },
                            howpublished = { \url{ https://universe.roboflow.com/fracture-detection/tuberculosis-apcje } },
                            url = { https://universe.roboflow.com/fracture-detection/tuberculosis-apcje },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-04-29 },
                            }