DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images

Macular edema (ME) is one of the most common retinal diseases that occur as a result of the detachment of the retinal layers on the macula. This study provides computer-aided identification of ME for even small pathologies on OCT using the advantages of Deep Learning. The study aims to identify ME on OCT images using a lightweight explainable Convolutional neural networks (CNN) architecture by composing significant feature activation maps and reducing the trainable parameters. A CNN is an effective Deep Learning algorithm, which consists of feature learning and classification stages. The proposed model, DeepOCT, focuses on reaching high classification performances as well as popular pre-trained architectures using less feature learning and shallow dense layers in addition to visualizing the most responsible regions and pathology on feature activation maps. The DeepOCT encapsulates the blockmatching and 3D filtering (BM3D) algorithm, flattening the retinal layers to avoid the effects arising from different macula positions, and excluding non-retinal layers by cropping. DeepOCT identified OCT with ME with the rates of 99.20%, 100%, and 98.40% for accuracy, sensitivity, and specificity, respectively. The DeepOCT provides a standardized analysis, a lightweight architecture by reducing the number of trainable parameters, and high classification performances for both large- and small-scale datasets. It can analyze medical images at different levels with simple feature learning, whereas the literature uses complicated pre-trained feature learning architectures. (c) 2021 The Authors.