Early detection and classification of microaneurysms in retinal fundus images using sequential learning methods

Diabetic retinopathy is the most common diabetic eye disease and causes blindness if not treated on time. Microaneurysms are one of the first clinical signs of diabetic retinopathy and appear as small red dots on fundus images. The incidence of blindness can be reduced by detecting microaneurysms at an earlier stage. In this paper, a new method is proposed for detection of microaneurysms from the colour fundus retinal images to assist the eye care specialist to examine large populations of patient. The microaneurysms are detected from the colour fundus image by applying the pre-processing techniques in order to remove the optic disc and similar blood vessels using morphological operations. The pre-processed image was then used for feature extraction, and these features were used for the purpose of classification. The classifiers used are support vector machine, Meta-cognitive Neural Network (McNN) and Self-adaptive Resource Allocation Network (SRAN), and their performances are compared and presented.