DOIONLINE 
DOIONLINE Publish In |
International Journal of Industrial Electronics and Electrical Engineering (IJIEEE)-IJIEEE |
 Journal Home Volume Issue |
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Issue |
Volume-7,Issue-8 ( Aug, 2019 ) | |||||||||
Paper Title |
Automatic Detection of Diabetic Retinopathy using CNN and R-CNN | |||||||||
Author Name |
Athira T R, Athira Sivadas, Aleena George, Amala Paul, Neethu Radha Gopan | |||||||||
Affilition |
Department of Electronics and Communication Engineering, Rajagiri School of Engineering and Technology, Cochin, India 5Assistant Professor, Department of ECE, Rajagiri School of Engineering and Technology, Cochin, India | |||||||||
Pages |
34-38 | |||||||||
Abstract |
Diabetic Retinopathy (DR) is an eye abnormality caused as a result of long term diabetes. As the disease progresses it leads to distortion and blurred vision. The diagnosis of DR using color fundus image requires skilled clinicians to identify the presence of critical features which makes this a difficult and time consuming task. In our paper we propose a CNN and R-CNN approach to diagnose DR from digital fundus images. In our method we have used 9 layers for CNN classification, trained it on 180 fundus images and tested on 250 images. All the images were classified into two groups i.e., with DR and without DR. The accuracy of CNN was found to be 86%. In our second phase we implemented a new approach were the whole image was segmented and only the regions of interest were taken for further processing. This R-CNN (Regional CNN) approach was found to be more efficient in terms of speed and accuracy. An accuracy of approximately 92% was obtained from R-CNN. Keywords - Convolutional Neural Network, Regional Convolutional Neural Network, Diabetic Retinopathy. | |||||||||
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