HEp-2 image classification using a multi-class and multiple-binary classifier

In medicine, identifying the indirect immunofluorescence of human epithelial type 2 (HEp-2) cells plays a decisive role in the diagnosis of autoimmune diseases. The manual interpretation of Hep-2 cell images may lead to some limitations, such as subjectivity, inconsistency and low efficiency. Therefore, it is very important to automatically identify HEp-2 images. Inspired by the outstanding performance of neural networks in image classification tasks, we propose a multi-class and multiple-binary classifier (MCMBC) for the classification of HEp-2 cells. MCMBC is an ensemble learner that contains two kinds of sub-classifiers: multi-class (MC) and multiple-binary (MB). The MC sub-classifier adopts a multi-scale convolutional neural network (MSCNN) that increases the efficiency of information transmission between layers. On the basis of classification results of the MC sub-classifier on validation sets, we can find easy-to-confuse class pairs. An easy-to-confuse class pair is two classes that are not easy to be identified from each other. The MB sub-classifiers adopt multiple-binary pre-trained VGG16 networks that are used to deal with these class pairs. The final prediction for a sample possibly belonging to an easy-to-confuse class is decided by the assembled features extracted from the last fully connected layer of MC and the output of MB sub-classifiers. To evaluate the proposed model, experiments were conducted on the ICPR 2014 Task-2 dataset. Experimental results show that MCMBC performs better than the state-of-the-art method (84.68% vs. 83.35% on the criterion of average classification accuracy (ACA) and 82.89% vs. 82.67% on the criterion of mean classification accuracy (MCA)).