(R) — Featured Research
An AI-powered system for automated blood smear analysis and malaria parasite detection, blending computer vision and deep learning to support low-resource diagnostics in the field.
Focus Areas
(03 parts of the pipeline)
Slide quality evaluation
Automatic quality scoring of glass slides - rejecting blurry, underexposed or contaminated samples before diagnosis.
Staining quality grading
Automated deep-learning grading of malaria slide staining - detecting poor staining and recommending time adjustments to ensure diagnostic-quality slides.
AI-based parasite identification
Detecting and counting Plasmodium parasites in real time with YOLO-based object detection, producing clinician-ready reports.
- YOLO
- OpenCV
- PyTorch
- TensorFlow
- CNN
- FastAPI
Exploring multi-modal AI systems that combine medical imaging, clinical metadata and intelligent diagnostics — supporting healthcare innovation where it's needed most.