Gender Classification Dataset and Pre-Trained Model by Burapha University

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Explore Dataset

Here are a few use cases for this project:

Use Case 1: Medical Research and Analytics Researchers can use the "Gender" computer vision model to study the differences between male and female lumbar spine anatomy, which is useful in detecting patterns, understanding risk factors, and developing treatment plans for various spinal conditions and disorders.

Use Case 2: Sports Medicine and Rehabilitation The model can be used by sports physicians or physiotherapists to assess gender-specific lumbar spine differences, enabling them to craft custom rehabilitation plans for athletes and active individuals suffering from spine-related injuries or issues.

Use Case 3: Ergonomics and Occupational Health Employers and ergonomics specialists can utilize the "Gender" computer vision model to develop gender-specific seating and workplace design solutions, promoting better spinal health for their workers and potentially reducing the risk of work-related lumbar spine issues.

Use Case 4: Assisting Radiologists Radiologists can use the "Gender" computer vision model to efficiently detect the gender of the patient from lumbar spine x-rays, streamlining their workflow and minimizing diagnostic errors due to incorrect patient data and ensuring appropriate treatment pathways are recommended.

Use Case 5: AI-assisted Orthopedic Implant Design Orthopedic implant manufacturers can use the "Gender" computer vision model to study gender-specific differences in lumbar-spine anatomy, contributing to the development of gender-specific spinal implants that offer improved fit, functionality, and long-term 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{
                            gender-mdwsx_dataset,
                            title = { Gender Dataset },
                            type = { Open Source Dataset },
                            author = { Burapha University },
                            howpublished = { \url{ https://universe.roboflow.com/burapha-university-0lo6o/gender-mdwsx } },
                            url = { https://universe.roboflow.com/burapha-university-0lo6o/gender-mdwsx },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-04-28 },
                            }