  Optical coherence tomography (OCT) imaging is a well-known technology for
visualizing retinal layers and helps ophthalmologists to detect possible
diseases. Accurate and early diagnosis of common retinal diseases can prevent
the patients from suffering critical damages to their vision. Computer-aided
diagnosis (CAD) systems can significantly assist ophthalmologists in improving
their examinations. This paper presents a new enhanced deep ensemble
convolutional neural network for detecting retinal diseases from OCT images.
Our model generates rich and multi-resolution features by employing the
learning architectures of two robust convolutional models. Spatial resolution
is a critical factor in medical images, especially the OCT images that contain
tiny essential points. To empower our model, we apply a new post-architecture
model to our ensemble model for enhancing spatial resolution learning without
increasing computational costs. The introduced post-architecture model can be
deployed to any feature extraction model to improve the utilization of the
feature map's spatial values. We have collected two open-source datasets for
our experiments to make our models capable of detecting six crucial retinal
diseases: Age-related Macular Degeneration (AMD), Central Serous Retinopathy
(CSR), Diabetic Retinopathy (DR), Choroidal Neovascularization (CNV), Diabetic
Macular Edema (DME), and Drusen alongside the normal cases. Our experiments on
two datasets and comparing our model with some other well-known deep
convolutional neural networks have proven that our architecture can increase
the classification accuracy up to 5%. We hope that our proposed methods create
the next step of CAD systems development and help future researches. The code
of this paper is shared at https://github.com/mr7495/OCT-classification.
