Suggestions

Here are some ideas for Hackathon projects:

1. Sperm Motility Classification:

   • Develop a machine learning model to classify sperm motility patterns based on the videos.

   • Train the model to distinguish between categories such as progressive motility, non-progressive motility, and immobility.

   • Evaluate the model's performance and provide insights into the characteristics of healthy and abnormal sperm movement.

2. Motility Parameter Extraction:

   • Create an algorithm to extract quantitative motility parameters from the videos, such as velocity, straight-line distance, and curvilinear distance.

   • Analyse how these parameters vary among different sperm samples and identify potential correlations with fertility.

3. Deep Learning for Abnormality Detection:

   • Train a deep learning model to detect abnormalities or irregularities in sperm movement.

   • This could include identifying tail abnormalities, irregular trajectories, or other atypical behaviours that might impact fertility.

4. Temporal Analysis of Sperm Behaviour:

   • Perform a temporal analysis of sperm movement patterns over the duration of the videos.

   • Identify any trends, periodicities, or changes in motility over time that could be associated with specific stages of sperm life.

5. Real-time Motility Monitoring:

   • Develop a real-time monitoring system for sperm motility.

   • Utilize the trained model to analyse live video streams, providing immediate feedback on sperm quality and motility.