Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
This addresses the problem of expensive and inaccessible genetic tests for diagnosing hereditary hemolytic anemias in clinical labs, though it is incremental as it builds on existing CNN architectures and feature fusion techniques.
The paper tackled diagnosing hereditary hemolytic anemias by proposing a hybrid method combining CNN-extracted spatial features and FFT-based spectral features from Percoll gradients, achieving an average F1-score of 88% across classes.
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.