International Conference on Computer and Knowledge Engineering

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ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images

Authors :

Mohammad Rahimzadeh¹ Mahmoud Reza Mohammadi²

1- FaraAI Company 2- FaraAI Company

Keywords :

Optical Coherence Tomography, OCT Image Classification, Retinal Disease, CAD System, Convolutional Neural Network, Ensemble Learning, Spatial Resolution Learning, Capsule Network

Abstract :

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.