Diagnostic System Based on Deep Learning to Detect Diabetic Retinopathy

Doi: 10.36351/pjo.v40i3.1771

Abstract

Purpose:  To develop a machine learning based diabetic retinopathy screening system to help ophthalmologists for initial level screening.

Study Design:  Diagnostic accuracy study.

Place and Duration of Study:  Haldwani in a private hospital from January, 2023 to June, 2023.

Methods:  A total of 229 fundus images (people suffering from diabetic retinopathy)were used which had micro aneurysms, soft exudates, hard exudates and hemorrhages. We classified these images and pre-processed them by scaling, orienting, and color adjustments. With the help of various pre-processing techniques, we decreased the size of our dataset so that it can be handled efficiently by our model with optimal resources.Visual Geometry Group (VGG) is a type of pre-trained deep convolutional neural network (CNN). The term “deep” refers to the number of layers; the VGG-16 uses 16 and VGG-19 uses 19 convolutional layers respectively. The model was tested on fresh retinal dataset.

Results:  Our research has demonstrated promising results, achieving a high accuracy rate of 90% on a human dataset by utilizing VGG16 for feature extraction and a Logistic Regression classifier for classification.

Conclusion:  Ophthalmologists can utilize this machine learning based screening system for diabetic retinopathy screening.