I. Introduction
Nowadays the information amount the medic has to deal with is huge. Hence, a careful analysis of such a data is hardly possible. In the medical context, the problem arises while making the medical decision when the state of the patient has to be assigned to the initially known class. In most of the cases, the boundaries between the different abnormal classes are not straightforward which further add to the complexity [1]. These classification problems are specific in the case of ophthalmologic applications. In ophthalmology, eye fundus examinations are highly preferred for diagnosing the abnormalities and follow-up of the development of the eye disease. But the problem of diagnosis lies in the huge amount of examinations which has to be performed by the specialists to detect the abnormalities [2]. An automated system based on neural computing overcome this problem by identifying automatically all the images with abnormalities.