Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias

TL;DR

This study introduces a novel method combining Fourier transform spectral features with CNN-based spatial features to classify hereditary hemolytic anemias from Percoll gradient images, achieving high accuracy and interpretability.

Contribution

The paper presents a hybrid approach integrating spectral Fourier features with CNN spatial features for improved anemia classification from Percoll gradients.

Findings

AlexNet with late fusion of spectral features achieved 88% F1-score.

Fourier transform features enhance CNN classification performance.

Grad-CAM analysis provides insights into spatial feature importance.

Abstract

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.