Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, two models like Probabilistic Neural network (PNN) and Support vector machine (SVM) are described and their performances are compared. Experimental results show that PNN has an accuracy of 89.60% and SVM has an accuracy of 97.608 %. This infers that the SVM model outperforms the other model.

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& Diabetic retinopathy (DR) is an eye disease caused by complications of diabetes and it should be detected early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. Two types were identified: nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this study, to diagnose diabetic retinopathy, we have proposed a new EYENET model that was obtained by combining the modified probabilistic neural network (PNN) and a modified radial basis function neural network (RBFNN), and hence, it possesses the advantages of both models. The features such as blood vessels and hemorrhages of the NPDR image and exudates of the PDR image are extracted from the raw images using image-processing techniques and are fed to the classifier for classification. A total of 600 fundus images were used, out of which 400 were used for training, and 200 images were used for testing. Experimental results show that PNN has an accuracy of 96%, modified PNN has an accuracy of 97.5%, RBFNN has an accuracy of 93.5%, modified RBFNN has an accuracy of 95.5%, and the proposed EYENET model has an accuracy of 98.5%. This infers that our proposed model outperforms all other models.

Digital image watermarking is a major domain for hiding the biometric information, in which the watermark data are made to be concealed inside a host image imposing imperceptible change in the picture. Due to the advance in digital image watermarking, the majority of research aims to make a reliable improvement in robustness to prevent the attack. The reversible invisible watermarking scheme is used for fingerprint and iris multimodal biometric system. A novel approach is used for fusing different biometric modalities. Individual unique modalities of fingerprint and iris biometric are extracted and fused using different fusion techniques. The performance of different fusion techniques is evaluated and the Discrete Wavelet Transform fusion method is identified as the best. Then the best fused biometric template is watermarked into a cover image. The various watermarking techniques such as the Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD) and Bacterial Foraging Optimization Algorithm (BFOA) are implemented to the fused biometric feature image. Performance of watermarking systems is compared using different metrics. It is found that the watermarked images are found robust over different attacks and they are able to reverse the biometric template for Bacterial Foraging Optimization Algorithm (BFOA) watermarking technique. ª 2015 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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