Automated Classification of Retinal OCT Images Using a Deep Multi-Scale Fusion CNN

Abstract

The automated analysis of optical coherence tomography (OCT) images can play a crucial role in the diagnosis and management of retinal diseases. The wide variations of the retinal disease manifestations in terms of shape, size, texture and spatial location pose a huge challenge in designing reliable and efficient automated methods. Existing methods mostly use single-scale deep frameworks for encoding features from the OCT images to make a diagnosis decision. Such approaches potentially ignore the useful discriminative information in different scales. Therefore, in this paper, we propose a Deep Multi-scale Fusion Convolutional Neural Network (DMF-CNN) that can encode the multi-scaled disease characteristics and effectively combine them for reliable classification. Specifically, multiple CNNs with different receptive fields are used to obtain scale-specific feature representations from the OCT images. These representations are fused to mine the cross-scale powerful discriminative features for classification. A joint multi-loss optimization strategy is designed to collectively learn the scale-specific and cross-scale complementary information during training. The method is evaluated on two publicly available OCT databases (the UCSD and the NEH) and delivers state-of-the-art performance. An impressive overall accuracy of 96.03% and 99.60% is obtained for the UCSD and the NEH datasets, respectively. The outstanding performance and the improved generalization makes the method a reliable diagnostic aid for medical practitioners.