It can be very difficult and time consuming to detect cases of malarial parasites. "In cases of very low infection levels, just a single malaria parasite might appear among 100,000 red blood cells." This has been compared to finding a needle in a haystack. Automated microscopes can be used to provide standardised detection of diseases resulting in efficiency and quality gains.
"The optical microscope remains a widely-used tool for diagnosis and quantitation of malaria. An automated system that can match the performance of well-trained technicians is motivated by a shortage of trained microscopists. We have developed a computer vision system that leverages deep learning to identify malaria parasites in micrographs of standard, field-prepared thick blood films. The prototype application diagnoses P. falciparum with sufficient accuracy to achieve competency level 1 in the World Health Organization external competency assessment, and quantitates with sufficient accuracy for use in drug resistance studies. A suite of new computer vision techniques—global white balance, adaptive nonlinear grayscale, and a novel augmentation scheme—underpin the system’s state-of-the-art performance."
"The solution required a combination of both deep learning and traditional computer algorithms used for segmenting things of interest within images."
Achieved same accuracy as trained microscopists