Manifold learning Object Detection Dataset and Pre-Trained Model by Manifold

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Explore Dataset

Here are a few use cases for this project:

Disease Diagnosis: The "Manifold learning" model can be applied in the medical field to facilitate early detection and diagnosis of oral diseases. By identifying and distinguishing between Adenoma, Hyperplasic, and Adenocarcinoma, this model can assist doctors in understanding a patient's condition and deciding the appropriate treatment method.
Medical Education: The model can be used as a learning tool in medical schools and universities for studying oral diseases. It can aid students in understanding and distinguishing the different classes of oral conditions, providing them with practical insights that textbooks might not offer.
Dental Consultation: Dentists can incorporate this model into their practice to identify and prevent potential oral diseases at a very early stage. This can improve the patient's oral health and could even prevent life-threatening situations with early detection of Adenocarcinoma.
Pharmaceutical Research: Pharmaceutical companies can use this model to analyze the effects of their drugs under development. By observing changes in the mouth before and after treatment, they can track the drug's efficacy in treating these oral conditions.
Telemedicine Applications: The model could be used in telehealth applications to enable remote oral health assessments. This can facilitate patients to get diagnostic reports from the comfort of their homes without physical visits to the hospital or clinic.

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{
                            manifold-learning_dataset,
                            title = { Manifold learning Dataset },
                            type = { Open Source Dataset },
                            author = { Manifold },
                            howpublished = { \url{ https://universe.roboflow.com/manifold/manifold-learning } },
                            url = { https://universe.roboflow.com/manifold/manifold-learning },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { oct },
                            note = { visited on 2024-04-27 },
                            }