Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods

International Journal of Electrical and Electronics Engineering

© 2022 by SSRG - IJEEE Journal

Volume 9 Issue 12

Year of Publication : 2022

Authors : V. Banupriya, S. Anusuya

10.14445/23488379/IJEEE-V9I12P104

How to Cite?

V. Banupriya, S. Anusuya, "Improving Classification of Retinal Fundus Image Using Flow Dynamics Optimized Deep Learning Methods," SSRG International Journal of Electrical and Electronics Engineering, vol. 9,  no. 12, pp. 39-48, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P104

Abstract:

Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina. This may endanger the subjects' vision if they have diabetes. It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness. Automated detection of the DR can be an extremely challenging task. Convolutional Neural Networks (CNN) are also highly effective at classifying images when applied in the present situation, particularly compared to the handmade and functionality methods employed. In order to guarantee high results, the researchers also suggested a cutting-edge CNN model that might determine the characteristics of the fundus images. The features of the CNN output were employed in various classifiers of machine learning for the proposed system. This model was later evaluated using different forms of deep learning methods and Visual Geometry Group (VGG) networks). It was done by employing the images from a generic KAGGLE dataset. Here, the River Formation Dynamics (RFD) algorithm proposed along with the FUNDNET to detect retinal fundus images has been employed. The investigation's findings demonstrated that the approach performed better than alternative approaches.

Keywords:

Diabetic Retinopathy (DR), Retinal Fundus Images, Deep Learning (DL), Visual Geometry Group (VGG) Network, Residual Networks (ResNet), and River Formation Dynamics (RFD).

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