Multi-classification of retinal diseases using a pyramidal ensemble deep framework

Retinal disorders diagnosis is of immense importance for appropriate treatment, i.e., accurate personalized medicine. In this work, a multi-resolutional feature ensemble approach is developed for retinal image classification using optical coherence tomography (OCT) images. Particularly, feature-rich pipeline using pyramidal architecture is designed to extract features from multi-scale inputs using partially-connected networks (PCNet). In addition, higher-order reflectivity features are extracted from the input images and are fused with pyramidal features for classification. The advantage of the hierarchical PCNet structure is that it allowed our system to extract multi-scale information to help in such task, all-at-once classification of the normal and abnormal retina. Namely, the larger input sizes give more global information, while the small inputs focus on local details. Evaluation on public OCT data set of four classes (normal, diabetic macular edema (DME), choroidal neovascularization (CNV), and drusen) and comparison against recent networks demonstrates not only the advantages of the proposed architecture's ability to produce feature-rich classification, but also highlights tangible advantages, such as network parameter reduction, enhanced feature learning and information flow, while reducing the risk of over fitting.