Multi‐instance inflated 3D CNN for classifying urine red blood cells from multi‐focus videos
Xinyu Li, Ming Li, Yongfei Wu, Xiaoshuang Zhou, Lifeng Zhang, Xinbo Ping, Xingna Nina Zhang, Wen Zheng · IET Image Processing · 2022
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.