Real-time Malaria Parasite Screening in Thick Blood Smears for Low-Resource Setting

We present a low-cost alternative and complementary solution for rapid malaria screening for low resource settings to potentially reduce the dependence on manual microscopic examination. We develop an image processing pipeline using a modified YOLOv3 detection algorithm to run in real time on low-cost devices. We test the performance of our solution on two datasets. In the dataset collected using a microscope camera, our model achieved 99.07% accuracy and 97.46% accuracy on the dataset collected using a mobile phone camera. While the mean average precision of our model is on par with human experts at an object level, we are several orders of magnitude faster than human experts as we can detect parasites in images as well as videos in real time.
Source: Journal of Digital Imaging - Category: Radiology Source Type: research