Coherent convolution neural network based retinal disease detection using optical coherence tomographic images

An optical coherence tomography images are used to visualize the retinal micro-architecture and perform an easy scan of its abnormalities. In this paper, a coherent convolutional neural network is proposed for four-class classification of retinal diseases and able to detect neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL class label in the OCT images. The new proposal overcomes three of the challenges by (1) more profoundly detect the irregular patterns of each class of retinal disease (2) manages consistency between input and output of the network (3) cohesively bound the layers of the network for easy flow of image features. The proposed convolution neural network model is having five layers. In order to adopt coherent behavior, the proposed model inculcating the batch normalization layer along with the every activity layer and obtained an accuracy of 97.19% for retinal disease detection. Moreover, the performance of this method is remarkably good as compared to other standard deep learning methods. This proposal is a promising step in revolutionizing the present scenario of ocular diagnostic system and has the potential to generate a significant clinical impact. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

In this study, a coherent convolutional neural network model is proposed for the classification and detection of retinal diseases. The model is able to effectively identify different categories of retinal diseases and achieves high accuracy.