Abstract

Info

Title:: Identification of sublethally damaged red blood cells based on bag-of-visual words model

Author(s):: ZHENG Kang; YUAN Yuhan; GU Xuelian; BAO Rui; ZHENG Yu; YANG Yuju; School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Keywords:: Keywords: sublethally damaged red blood cell bag-of-visual words model support vector machine automatic identification

Abstract:: Abstract: A method for the automatic identification of sublethally damaged red blood cells (RBC) according to their morphological changes is proposed. The images of blood cells during cardiopulmonary bypass are analyzed in the study, including 2 763 images of sublethally damaged RBC and 2 507 images of normal RBC. The bag-of-visual words is used as the feature extraction method of RBC, and the support vector machine models of polynomial kernel, Gaussian kernel and Sigmoid kernel function are selected separately. A 5-fold cross validation method is adopted to verify the performance of the method, taking precision rate, recall rate and F1 score as evaluation indexes. The results show that the identification accuracies under 3 different kernel models are 91.05%±0.82%, 94.16%±0.50% and 85.60%±0.94%, respectively. The proposed method can effectively distinguish the sublethally damaged RBC and provide a scheme for the automatic identification of sublethally damaged RBC.