Wavelet-based Convolutional Mixture of Experts model: An application to automatic diagnosis of abnormal macula in retinal optical coherence tomography images

This paper presents a new fully automatic algorithm for classification of 3D Optical Coherence Tomography (OCT) images as Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), and healthy people. The proposed algorithm does not need to any retinal layer alignment and also segmentation processes (e.g., segmentation of intra-retinal layers and lesion structures). The algorithm utilizes a new Wavelet-based Convolutional Mixture of Experts (WCME) model as an adaptive feature extraction and classification method. The WCME benefits from spatial-frequency decomposition and also an ensemble of convolutional neural networks (CNNs) to build a high-level representation of OCT data. In this study, a retinal OCT data set constituted of 148 cases is used for evaluation of the method based on unbiased cross-validation approach. The dataset consists of 50 normal, 50 DME, and 48 AMD OCT acquisitions from Heidelberg device. With the proposed WCME model, the overall algorithm accurately classified the OCT data with a precision rate of 95.21% and an area under the ROC curve (AUC) of 0.986.