| Title: | Classification and Detection of Diabetic Retinopathy. Advances in Intelligent Analysis of Medical Data and Decision Support Systems, Studies in Computational Intelligence, Springer, Volume 473, 2013, pp 135-145. DOI: 10.1007/978-3-319-00029-9_12 |
| Authors: | Azar AT, Balas VE |
| Year: | 2013 |
| Keywords: | Not Available |
| Journal: | Not Available |
| Volume: | Not Available |
| Issue: | Not Available |
| Pages: | Not Available |
| Publisher: | Not Available |
| Local/International: | International |
| Paper Link: | |
| Full paper | Not Available |
| Supplementary materials | Not Available |
| Abstract: |
Diabetic retinopathy (DR) is the leading cause of blindness in adults around the world today. Early detection (that is, screening) and timely treatment have been shown to prevent visual loss and blindness in patients with retinal complications of diabetes. The basis of the classification of different stages of diabetic retinopathy is the detection and quantification of blood vessels and hemorrhages present in the retinal image. In this paper, the four retinal abnormalities (microaneurysms, haemorrhages, exudates, and cotton wool spots) are located in 100 color retinal images, previously graded by an ophthalmologist. A new automatic algorithm has been developed and applied to 100 retinal images. Accuracy assessment of the classified output revealed the detection rate of the microaneurysms was 87% using the thresholding method, whereas the detection rate for the haemorrhages was 88%. On the other hand, the correct classification rate for microaneurysms and haemorrhages using the minimum distance classifier was 60% and 94% respectively. The thresholding method resulted in a correct detection rate for exudates and cotton wool spots of 93% and 89% respectively. The minimum distance classifier gave a correct rate for exudates and cotton wool spots of 95% and 86% respectively. |
