2021/03/17 by Ario Sadafi, Lucía María Moya Sans, Sadafi, Ario +13
Computer Science · Engineering · Medicine · #Blood groups and transfusion #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #Erythrocyte Function and Pathophysiology #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2103.09671
Accepted for publication at the 2021 IEEE International Symposium on Biomedical Imaging (ISBI 2021)
arxiv created 2021/03/17 · openalex publication_date 2021/03/17 · arxiv updated 2021/03/18 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.