  Hereditary hemolytic anemias are genetic disorders that affect the shape and
density of red blood cells. Genetic tests currently used to diagnose such
anemias are expensive and unavailable in the majority of clinical labs. Here,
we propose a method for identifying hereditary hemolytic anemias based on a
standard biochemistry method, called Percoll gradient, obtained by centrifuging
a patient's blood. Our hybrid approach consists on using spatial data-driven
features, extracted with a convolutional neural network and spectral
handcrafted features obtained from fast Fourier transform. We compare late and
early feature fusion with AlexNet and VGG16 architectures. AlexNet with late
fusion of spectral features performs better compared to other approaches. We
achieved an average F1-score of 88% on different classes suggesting the
possibility of diagnosing of hereditary hemolytic anemias from Percoll
gradients. Finally, we utilize Grad-CAM to explore the spatial features used
for classification.
