  In this work we propose an approach to select the classification method and
features, based on the state-of-the-art, with best performance for diagnostic
support through peripheral blood smear images of red blood cells. In our case
we used samples of patients with sickle-cell disease which can be generalized
for other study cases. To trust the behavior of the proposed system, we also
analyzed the interpretability.
  We pre-processed and segmented microscopic images, to ensure high feature
quality. We applied the methods used in the literature to extract the features
from blood cells and the machine learning methods to classify their morphology.
Next, we searched for their best parameters from the resulting data in the
feature extraction phase. Then, we found the best parameters for every
classifier using Randomized and Grid search.
  For the sake of scientific progress, we published parameters for each
classifier, the implemented code library, the confusion matrices with the raw
data, and we used the public erythrocytesIDB dataset for validation. We also
defined how to select the most important features for classification to
decrease the complexity and the training time, and for interpretability purpose
in opaque models. Finally, comparing the best performing classification methods
with the state-of-the-art, we obtained better results even with interpretable
model classifiers.
