الفهرس 
AimsOnly 14 pages are availabe for public view
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Abstract
Diabetic retinopathy (DR) is one of the leading causes of vision loss. It causes neovascularization with blocking the regular small blood vessels. Early detection helps the ophthalmologist in patient treatment and prevents or delays vision loss. In this thesis, multifractal analysis has been used in some details to automate the diagnosis of diabetic without diabetic retinopathy and non-proliferative DR. Concerning using number of multifractal geometrical methods, as a necessary second step the enforcement of the sophisticated artificial neural network has been consultant in order to improve the accuracy of the obtained results. Patients and methods: 120 normal cases’ eyes, 60 diabetic without DR patients’ eyes and 60 non-proliferative diabetic retinopathy (mild to moderate) eyes are exposed to optical coherence tomography angiography (OCTA) to get image superficial layer of macula for all cases. Extracting the most changeable features that associated to the morphological retinal vascular network alternations as the generalized dimensions, α at the maximum f (α) singularity spectrum, the spectrum width, the spectrum symmetrical shift point and lacunarity. These seven extracted features are related to the multifractal analysis results, which describe the vascular network architecture and gaps distribution. A supervised Artificial Neural Network (ANN) is used to classify the images into three categories: normal, diabetic without diabetic retinopathy and non-proliferative DR. The ANN obtains a single value decision with classification accuracy 97.78% with minimum sensitivity 96.67%. This approach could promote risk stratification for the decision of early diagnosis of diabetic retinopathy.