Diabetic eye disease in computerised tomography of feature extraction and classification in hybrid neural network

Abstract

ABSTRACT Blindness or loss of vision is primarily caused by diabetic retinopathy, or DR. It is a retinal condition that may cause harm to the eyes’ blood vessels.Due to the complicated eye structure, manually detecting diabetic retinopathy takes time and is prone to human mistake. There have been several automatic methods proposed for the diagnosis of diabetic retinopathy from fundus images.Using a combination of InceptionV3 and ResNet50, an optimised deep Convolutional Neural Network model of feature fusion is presented in this paper to categories diabetic retinopathy photos into five kinds. Concatenating results in the suggested model including both the Inception Modules and the Residual Blocks, this mitigates the gradient problem and reduces the number of parameters. Resnet50 and Inceptionv3, two different deep learning (DL) models, were used. The proposed hybrid InceptionV3 and ResNet50 based optimised deep CNN (IR_ODCNN) model gathers features from the data and joins them before sending them to the DCNN that has been optimised for classification. The hyper parameters for CNN layer training are optimised using the Grey Wolf Optimizer (GWO) method. An image dataset of the fundus is used to assess the suggested model. The experimental findings indicate that, when compared to the current approaches, In terms of classifying diabetic retinopathy, the proposed model performs better.