Hereditary hemolytic anemias are genetic disorders that af-fect the shape and density of red blood cells. Genetic testscurrently used to diagnose such anemias are expensive andunavailable in the majority of clinical labs. Here, we pro-pose a method for identifying hereditary hemolytic anemiasbased on a standard biochemistry method, called Percollgradient, obtained by centrifuging a patient’s blood. Our hy-brid approach consists on using spatial data-driven features,extracted with a convolutional neural network and spectralhandcrafted features obtained from fast Fourier transform.We compare late and early feature fusion with AlexNet andVGG16 architectures. AlexNet with late fusion of spectralfeatures performs better compared to other approaches. Weachieved an average F1-score of 88% on different classes sug-gesting the possibility of diagnosing of hereditary hemolyticanemias from Percoll gradients. Finally, we utilize Grad-CAM to explore the spatial features used for classification.