  We introduce a machine learning-based method for fully automated diagnosis of
sickle cell disease of poor-quality unstained images of a mobile microscope.
Our method is capable of distinguishing between diseased, trait (carrier), and
normal samples unlike the previous methods that are limited to distinguishing
the normal from the abnormal samples only. The novelty of this method comes
from distinguishing the trait and the diseased samples from challenging images
that have been captured directly in the field. The proposed approach contains
two parts, the segmentation part followed by the classification part. We use a
random forest algorithm to segment such challenging images acquitted through a
mobile phone-based microscope. Then, we train two classifiers based on a random
forest (RF) and a support vector machine (SVM) for classification. The results
show superior performances of both of the classifiers not only for images which
have been captured in the lab, but also for the ones which have been acquired
in the field itself.
