Red blood cells of sickle cell disease patients have diverse shapes that reveal important biomechanical and bio-rheological characteristics. Having an effective way of classifying red blood cell shapes will lead to better prognosis of the disease. Xu et al. develop a new computational framework based on deep convolutional networks in order to efficiently classify the heterogeneous shapes encountered in the sickle blood.
Image credit: Xu et al.