Machine Learning for Automated Cell Counting in Healthcare
Our team developed an automated cell counting solution using machine learning for university researchers. The system processes and analyzes large volumes of microscopy images, reducing manual errors and improving research productivity.
Convolutional Neural Networks (CNN) for accurate cell counting
Retrainable platform for adapting to new cell types
Integration with microscopy imaging software for real-time analysis
Challenge
Manual counting of cells in microscopy images was slow and error-prone, limiting the number of images researchers could process and reducing the overall efficiency of lab operations.
Solution
We implemented a machine learning solution that automates cell counting using CNNs, eliminating the need for manual counting. The system also features a retrainable platform, allowing it to be adapted for counting various cell types in different research projects.
Results
The automated solution doubled the number of images processed by the lab, significantly reducing cell counting errors and increasing research efficiency.
Other projects you might be interested in: