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

Single Test Image-Based Automated Machine Learning System for Distinguishing between Trait and Diseased Blood Samples