TO IMPROVE THE RESULT OF AUTOMATIC DETECTION OF DIABETIC RETINOPATHY

Monika Rajput; Rohit Mahajan

TO IMPROVE THE RESULT OF AUTOMATIC DETECTION OF DIABETIC RETINOPATHY

Authors: Monika Rajput, Rohit Mahajan

Unique Paper ID: 147336
Volume: 5
Issue: 7
PageNo: 148-153

Keywords: Diabetic retinopathy CNN MSVM Gaussian Filtering fundus images

Abstract:
Diabetic retinopathy detection at early stage is critical and ignorance could lead to loss of vision also. Diabetic retinopathy detection at early stage is the prime objective of this study. To detect the diabtetic retinopathy at early stage, deep learning mechanism is collaborated with the Gaussian filtering and multi support vector machine. Multi Support vector machine is used at classification phase. Simulation is conducted within MATLAB 2017 using image processing toolbox. Operation is performed in coloured fundus images. To tackle the problems of large image set, deep learning mechanism is merged along with genetic approach. Layers are defined including input, processing and output layers along with filtering layer. Once output is generated using this network, it is fed into the CNN for segmentation. Genetic algorithm with MSVM is used at classification section to improve the accuracy of overall result. Classification results shows improvement by 2 % in terms of specificity, accuracy and error rate.

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Cite This Article

ISSN: 2349-6002
Volume: 5
Issue: 7
PageNo: 148-153

TO IMPROVE THE RESULT OF AUTOMATIC DETECTION OF DIABETIC RETINOPATHY

Available:https://ijirt.org/Article?manuscript=147336

13 Aug, 2018

COMPARATIVE ANALYSIS OF TECHNIQUES USED TO DETECT DIABETIC RETINOPATHY AT EARLY STAGE

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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