|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|504890||864447||2015||19 صفحه PDF||سفارش دهید||دانلود کنید|
• A referral system for diabetic retinopathy can decrease the load on ophthalmologists.
• A referral system is developed by using combination of mathematical techniques.
• Referral system makes treatment of patient cost and time effective.
• The referral system has been tested on four fundus databases.
• We have examined the referral system using in two different scenarios.
Hard exudates are one of the most common anomalies/artifacts found in the eye fundus of patients suffering from diabetic retinopathy. These exudates are the major cause of loss of sight or blindness in people having diabetic retinopathy. Diagnosis of hard exudates requires considerable time and effort of an ophthalmologist. The ophthalmologists have become overloaded, so that there is a need for an automated diagnostic/referral system. In this paper a referral system for the hard exudates in the eye-fundus images has been presented. The proposed referral system works by combining different techniques like Scale Invariant Feature Transform (SIFT), K-means Clustering, Visual Dictionaries and Support Vector Machine (SVM). The system was also tested with Back Propagation Neural Network as a classifier. To test the performance of the system four fundus image databases were used. One publicly available image database was used to compare the performance of the system to the existing systems. To test the general performance of the system when the images are taken under different conditions and come from different sources, three other fundus image databases were mixed. The evaluation of the system was also performed on different sizes of the visual dictionaries. When using only one fundus image database the area under the curve (AUC) of maximum 0.9702 (97.02%) was achieved with accuracy of 95.02%. In case of mixed image databases an AUC of 0.9349 (93.49%) was recorded having accuracy of 87.23%. The results were compared to the existing systems and were found better/comparable.
Journal: Computers in Biology and Medicine - Volume 64, 1 September 2015, Pages 217–235