Diabetic retinopathy detection using deep learning
M.BUVANESWARI
, R.SOWNDARYA LAKSHMI , A.SUJITHA
deep learning, support vector machine ,PCA
Diabetic retinopathy is when damage occurs to the retina due to diabetes, which affects up to 80 percent of all patients who have had diabetes for 10 years or more. The expertise and equipment required are often lacking in areas where diabetic retinopathy detection is most needed. Most of the work in the field of diabetic retinopathy has been based on disease detection or manual extraction of features, but this paper aims at automatic diagnosis of the disease into its different stages using deep learning. This paper presents the design and implementation of GPU accelerated deep convolutional neural networks to automatically diagnose and thereby classify high- resolution retinal images into 5 stages of the disease based on severity. The single model accuracy of the convolutional neural networks presented in this paper is 0.386 on a quadratic weighted kappa metric and ensembling of three such similar models resulted in a score of 0.3996.
"Diabetic retinopathy detection using deep learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 6, page no.363 - 366, June-2022, Available :https://ijsdr.org/papers/IJSDR2206060.pdf
Volume 7
Issue 6,
June-2022
Pages : 363 - 366
Paper Reg. ID: IJSDR_200651
Published Paper Id: IJSDR2206060
Downloads: 000347254
Research Area: Computer Engineering
Country: Nammakkal, TamilNadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
