Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests

Automatic image classification systems for indirect immunofluorescence (IIF) labeling of human epithelial (HEp-2) cell specimens are needed to improve the efficient management of autoimmune diseases. In this paper, we propose to classify HEp-2 cell specimen imagery using a combination of local features and deep learning features extracted from the IIF images. Two local descriptors are used to capture texture information, namely: Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) extending the LBP descriptor and Joint Motif Labels (JML) based on the Peano scan motif concept. Deep learning features are then extracted using the VGG-19 image classification network. Finally, all descriptors are combined using a late fusion approach with a Random Forests (RF) classifier with seven output classes. Experimental results show that our proposed framework achieves a mean class accuracy of 92.11% with five-fold cross validation using the RF classifier with 1000 trees on the HEp-2 specimen benchmark dataset, which outperforms the state-of-the-art accuracy on this dataset.