A Novel Detection System for Human Retina Based on Fuzzification Neural Network. Automated methods are based on human biometrics for the purpose of verifying a personxe2x80x99s identity based on the physiological or behavioural features of humans. Biometrics traits include facial, iris, retina, fingerprint, and tongue. Preserving biometrics is very important because it is one of the means of preserving the information of others. One of the most important biometrics, most accurate, and powerful methods of identity verification is human retina. The unique retina features per person can be displayed as a compact binary form. For the purpose of confirming an individualxe2x80x99s identity, the unique retinal features of the individual can be easily compared with the reference template. In this paper, a novel detection system is proposed for retinal images utilising the fuzzification model that integrated with deep learning neural networks. In addition, identification system based on human retina is considered the most protect scheme of modern applications. The novelty in this paper is integrated between fuzzy set and deep learning neural networks. Practically, this is first time to integrate between fuzzification model and artificial neural network. The obtained experimental results of the proposed identification system demonstrate that the suggested approach possess outstanding detection rate with decline error rate as well as reducing the number of false alarms.

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.