  Effectively determining malaria parasitemia is a critical aspect in assisting
clinicians to accurately determine the severity of the disease and provide
quality treatment. Microscopy applied to thick smear blood smears is the de
facto method for malaria parasitemia determination. However, manual
quantification of parasitemia is time consuming, laborious and requires
considerable trained expertise which is particularly inadequate in highly
endemic and low resourced areas. This study presents an end-to-end approach for
localisation and count of malaria parasites and white blood cells (WBCs) which
aid in the effective determination of parasitemia; the quantitative content of
parasites in the blood. On a dataset of slices of images of thick blood smears,
we build models to analyse the obtained digital images. To improve model
performance due to the limited size of the dataset, data augmentation was
applied. Our preliminary results show that our deep learning approach reliably
detects and returns a count of malaria parasites and WBCs with a high precision
and recall. We also evaluate our system against human experts and results
indicate a strong correlation between our deep learning model counts and the
manual expert counts (p=0.998 for parasites, p=0.987 for WBCs). This approach
could potentially be applied to support malaria parasitemia determination
especially in settings that lack sufficient Microscopists.
