Diabetic retinopathy (DR) is a micro vascular problem caused by diabetes that can lead to loss of sight. The early detection of diabetic retinopathy is important to avoid the severity of sightlessness. In this manuscr...
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Diabetic retinopathy (DR) is a micro vascular problem caused by diabetes that can lead to loss of sight. The early detection of diabetic retinopathy is important to avoid the severity of sightlessness. In this manuscript, a comparative analysis of several deep learning methods for DR identification is proposed. The input fundus images are taken from a standard dataset pre-processed by the Mathematical Morphology process. Moreover, the images are segregated using a Multilevel segmentation of the Region of interest (ROI) based on the split and merge algorithm. After that, an original deep learning architecture is utilized to categorize the segregated fundus images. Deep learning methods, such as Convolution neural network (CNN), Recurrent Neural Network (RNN), Support Vector Machine (SVM), Fuzzy K-means cluster (FKM) and Discriminant Analysis (DA) are proposed to classify the DR. The proposed DR identification and detection with CNN provides 65.54% SP, 100% SE, 78.54% SV and 96.95% ACC. Finally, CNN shows better performance than other classifiers.
An algorithm for separating several sources with fewer sensors in a nonstationary environment is presented. The nonstationary environment is. modelled by tracking unmixing matrix classes and the optimal class number i...
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An algorithm for separating several sources with fewer sensors in a nonstationary environment is presented. The nonstationary environment is. modelled by tracking unmixing matrix classes and the optimal class number is determined using a modified split and merge algorithm. Its usefulness is demonstrated by considering a teleconferencing problem.
This paper presents a new scheme for fractal image compression based on adaptive Delaunay triangulation, Such a partition is computed on an initial set of points obtained with a split and merge algorithm in a grey lev...
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This paper presents a new scheme for fractal image compression based on adaptive Delaunay triangulation, Such a partition is computed on an initial set of points obtained with a split and merge algorithm in a grey level dependent way, The triangulation is thus fully flexible and returns a limited number of blocks allowing good compression ratios, Moreover, a second original approach is the integration of a classification step based on a modified version of the Lloyd algorithm (vector quantization) in order to reduce the encoding complexity, The vector quantization algorithm is implemented on pixel histograms directly generated from the triangulation. The aim is to reduce the number of comparisons between the two sets of blocks involved in fractal image compression by keeping only the best representative triangles in the domain blocks set, Quality coding results are achieved at rates between 0.25-0.5 b/pixel depending on the nature of the original image and on the number of triangles retained.
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