Coarse-to-Fine Two-Stage Convolutional Neural Network Algorithm

In medicine, identifying the indirect immunofluorescence of human epithelial type 2 (HEp-2) cells plays a decisive role in the diagnosis of autoimmune diseases, which is limited by burden of human and material resources. Inspired by the outstanding performance of neural network in image classification tasks, a coarse-to-fine two-stage convolutional neural network (CTFTCNN) is proposed using clustering algorithm for classifying HEp-2 cells. In the proposed method, there are two types of classification tasks: coarse-grained classification and fine-grained classification. In coarse-grained classification, a clustering algorithm is first used to generate a coarse-grained dataset from the original dataset, and then a multi-scale convolutional neural network (MSCNN) is used to process the coarse-grained dataset. Next, fine-grained classification is performed under certain conditions. If each coarse class in coarse-grained dataset contains at least two fine classes, the coarse class will be subdivided further by using a VGG16 network. Finally, the two tasks handled by the coarse-grained and the fine-grained networks are combined together. For a coarse class that contains at least two fine classes, the features extracted from both the coarse-grained and fine-grained networks are merged to determine the final prediction result. Experiments are conducted on the real-world dataset to evaluate the proposed model. Experimental results show that CTFTCNN is promising compared with the state-of-the-art methods.