International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 179 - Issue 48 |
Published: Jun 2018 |
Authors: Gargi Kashyap |
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Gargi Kashyap . Analysis of Diabetic Retinopathy by Detecting Exudates in the Retinal image. International Journal of Computer Applications. 179, 48 (Jun 2018), 35-38. DOI=10.5120/ijca2018917290
@article{ 10.5120/ijca2018917290, author = { Gargi Kashyap }, title = { Analysis of Diabetic Retinopathy by Detecting Exudates in the Retinal image }, journal = { International Journal of Computer Applications }, year = { 2018 }, volume = { 179 }, number = { 48 }, pages = { 35-38 }, doi = { 10.5120/ijca2018917290 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2018 %A Gargi Kashyap %T Analysis of Diabetic Retinopathy by Detecting Exudates in the Retinal image%T %J International Journal of Computer Applications %V 179 %N 48 %P 35-38 %R 10.5120/ijca2018917290 %I Foundation of Computer Science (FCS), NY, USA
Retinal image investigation and processing has great significance in clinical study. Digital retinal photograph is widely used for screening of the patients suffering from diseases such as Diabetic Retinopathy and Glaucoma. A common issue of diabetes is diabetic eye disease. Diabetes is the most common disease in India. Timely diagnosis and management of these diseases can prevent severe visual loss. The proposed work will mainly deal with the detection of clinical components exudates from the retinal images, which are of prime importance in the clinical examination of diabetic patients for automated diagnosis of retinal diseases. The proposed method has been chosen in order to reduce the time consumption, cost for detection of such diseases and to provide an easy, simple and efficient way. The Sensitivity and Specificity of the proposed work for analysis of disease in the retinal images of the patients suffering from DR from local hospital (Sankaradeva Nethralaya) are 96.9% and 98.4% and for STARE database are 95% and 97.5% respectively.