Multi‐instance inflated 3D CNN for classifying urine red blood cells from multi‐focus videos

Abstract

Classifying urine red blood cells (U-RBCs) is the core operation in diagnosing urinary system diseases (USDs). In this paper, based on a novel data type named multi-focus video, a multi-instance inflated 3D convolutional neural network (MI3D) is proposed. In order to accurately classifying U-RBCs, the MI3D integrates inflated inception-V1 with multi-instance learning models. Compared with the existent U-RBC classification methods relying on single focus images, the MI3D using multi-focus videos effectively avoids the misclassification caused by the significant deformation of U-RBCs with the focus of microscope changing. In addition, the MI3D can learn the typical shapes and deformation patterns of U-RBCs from multi-focal videos simultaneously. Therefore, the accuracy of MI3D exceeds the mainstream video classification models. There are totally 597 multi-focus videos that include four types of U-RBCs collected to verify the effectiveness of MI3D. Experimental results show that the classification accuracy of MI3D is inspiring with 94.4%, which is obviously higher than that of existed U-RBC classification method (85.6%). The accuracy of MI3D also achieves the comparable level with the results by junior microscopist (95.6%). Lastly, the MI3D has powerful real-time performance, whose classification speed reaches 1.4 times than that of the microscopist.