Proposing an Automated System for Differentiating between Healthy Individuals and Patients with Diabetic Retinopathy

Subject Areas : Renewable energy

Mina Ghayoor ¹ (Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Hossein Pourghassem ² (Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran)

Received: 2019-10-31 Accepted : 2020-09-15 Published : 2021-02-19

Keywords: Support vector machine, principal component analysis, : Diabetic retinopathy, shape and color properties of images,

Abstract :

Diabetes is one of the most common diseases in the world, adversely affects different body organs. One of the most common causes of eye problems is diabetes. Analyzing retinal damage is one of the best ways to diagnose diabetes so one of the best ways to diagnose diabetes is to look at the damage to the retina. Hence, first, a highly applicable and effective method, which is a combination of the Wiener filter and the discrete wavelet transform (DWT), is used for the removal of noise from images. Afterward, the k-means clustering algorithm is used to remove the bad image sections including very light and very dark areas of the image. Next, the image color and shape features are extracted. We transfer the images to the lab space, which fits the eye more, to extract the image color features. To extract the image shape features, first the images are converted into grey images and then the shape features are extracted. After extracting the features, the number of features is reduced using the Principal Component Analysis (PCA) algorithm. Besides, the best and most effective features are also selected. Finally, the support vector machine classifier with different kernel is used to classify the features and images into two categories, namely the healthy participants and patients. The accuracy resulting from this algorithm using the test images is over 90%.

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