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A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification
Authors:Farrukh Zia  Isma Irum  Nadia Nawaz Qadri  Yunyoung Nam  Kiran Khurshid  Muhammad Ali  Imran Ashraf  Muhammad Attique Khan
Affiliation:1.Department of ECE, COMSATS University Islamabad, Wah Campus, 47040, Pakistan2 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea3 Department of Electrical Engineering, NUML, Rawalpindi, Pakistan4 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
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
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