Malaria Parasite Detection Using Deep Learning
A computer-vision research project exploring deep-learning methods for detecting malaria parasites in blood-smear imagery.
Placeholder - Licensed microscopy imagery or actual model outputs to be added
Overview
This work explores CNN-based approaches and object-detection architectures including YOLO and EfficientDet for automated malaria parasite detection in microscopy images.
Our Approach
Train and evaluate object detection models on blood-smear microscopy datasets, comparing detection accuracy and model efficiency across architectures.
Methods & Technology
- Convolutional Neural Networks (CNN)
- YOLO object detection
- EfficientDet architecture
- Medical image preprocessing
- Model evaluation on microscopy data
Limitations & Considerations
This is research work, not a clinically validated diagnostic system. Requires proper dataset licensing and permissions. Clinical deployment would require extensive validation, regulatory approval, and integration with laboratory workflows.
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