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Published on: December 30, 2025
Automated identification of diabetic retinal exudates in digital colour images
A Osareh1, M Mirmehdi, B Thomas
1Department of Computer Science, University of Bristol, Merchant Ventures Building, Woodland Road, Bristol BS8 1UB, UK. a.osareh@bristol.ac.uk
Aim:
To identify retinal exudates automatically from colour retinal images.
Methods:
The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated.
Results:
The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification.
Conclusions:
This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy.
