AI-Powered Blood Cell Detection & Malaria Identification System

AI-powered detection of red blood cells, white blood cells, and platelets from blood samples.

Specialized malaria module for detecting Plasmodium-infected red cells.

End-to-end automation from image ingestion to analysis.

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Scalable model architecture deployable across lab networks.

Traditional manual blood cell counting and malaria detection are time-consuming, error-prone, and require skilled labor. There was a pressing need for an automated, scalable solution to accelerate diagnostics and improve reliability, especially in regions with high malaria incidence.

NeuraMonks developed a deep learning-based segmentation pipeline capable of identifying:

Red blood cells (RBCs)

White blood cells (WBCs)

Platelets

A separate malaria detection module was trained on stained blood smear datasets to flag infected RBCs. The system integrates seamlessly with lab hardware and supports real-time deployment, delivering results in seconds

Training models on varied image quality and lighting conditions.

Balancing precision and recall in malaria detection to minimize false negatives.

Avoiding cell overlap misclassification using instance segmentation techniques.

Efficiently deploying the solution for use in low-resource settings

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