Diabetic retinopathy detection using Bilayered Neural Network classification model with resubstitution validation

Abstract

In recent years, eye diseases in diabetic patients are one of the most common has been diabetic retinopathy (DR). which leads to complete blindness in advanced stages. Diabetes affects the blood vessels in the retina and causes vision loss. One of the ways to decrease the risk of this issue is to detect diabetic retinopathy in its early stages. This study describes a computer-aided screening system (DREAM) that uses a neural network classification model in machine learning to assess fundus images with different illumination and fields of vision and provide a severity grade for diabetic retinopathy. Moreover, the methodology of this study based on:•Enhancement techniques have been used on dataset images, histogram equalization, noise reduction and image scaling,•vSLAM has been selected as feature extraction,•Bilayered Neural Network under resubstitution validation used as a classification model. Finally, after testing on the DR severity grading system is tested on 6332 images of detection of diabetic retinopathy images, the result of the ROC curve is 0.985 on image dataset and obtains accuracy reached 98.5%. The classification result has been founded under MATLAB platform, beside it that be work with real time analysis and detection when patient eyes analysis.

Keywords: Diabetic Retinopathy Detection Using Bilayered Neural Network; Diabetic retinopathy (DR), Feature extraction; Machine learning; Resubstitution validation.

Graphical abstract

Fig. 1

Proposed Block Diagram of the DR Classification.

Fig. 2

Applied Histogram Equalization.

Fig. 3

Histogram Equalization Effect for Each Diabetic Retinopathy Stage.

Fig. 4

Applied Median Filter for Noise Reduce.

Fig. 5

Visual Simultaneous Localization Features.

Fig. 6

Structure of Neural Network Classifiers.

Fig. 7

Different Classes of Diabetic Retinopathy.

Fig. 8

Extracted Features Scatter.

Fig. 9

Confusion Matrix of Bilayered Neural Network.

Fig. 10

AUC of Bilayered Neural Network.