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A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Farrukh Zia1, Isma Irum1, Nadia Nawaz Qadri1, Yunyoung Nam2,*, Kiran Khurshid3, Muhammad Ali1, Imran Ashraf4, Muhammad Attique Khan4
1 Department of ECE, COMSATS University Islamabad, Wah Campus, 47040, Pakistan
2 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
3 Department of Electrical Engineering, NUML, Rawalpindi, Pakistan
4 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
* Corresponding Author: Yunyoung Nam. Email:
(This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2022, 70(2), 2261-2276. https://doi.org/10.32604/cmc.2022.017820

Received 12 February 2021; Accepted 19 April 2021; Issue published 27 September 2021

Abstract

Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients’ illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can train models with high accuracy. The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction (DR). The proposed deep learning framework utilizes the strength of selected models (VGG and Inception V3) by fusing the extracated features. To select the most discriminant features from a pool of features, an entropy concept is employed before the classification step. The deep learning models are fit for measuring the highlights as veins, liquid dribble, exudates, haemorrhages and miniaturized scale aneurysms into various classes. The model will ascertain the loads, which give the seriousness level of the patient’s eye. The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures.

Keywords

Deep neural network; diabetic retinopathy; retina; features extraction; classification

Cite This Article

F. Zia, I. Irum, N. Nawaz Qadri, Y. Nam, K. Khurshid et al., "A multilevel deep feature selection framework for diabetic retinopathy image classification," Computers, Materials & Continua, vol. 70, no.2, pp. 2261–2276, 2022.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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