Enforcing artificial neural network in the early detection of diabetic retinopathy OCTA images analysed by multifractal geometry

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 work, 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: Thirty normal cases’ eyes, 30 diabetic without DR patients’ eyes and 30 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. These images were approved in Ophthalmology Center in Mansoura University, Egypt, and medically were diagnosed by the ophthalmologists. We extract the most changeable features that associated to the morphological retinal vascular network alternations. The 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. Results: The human retinal blood vascular network architecture is found to be a fractal system. Multifractal geometry describes the irregularity and gaps distribution in the retina. We extracted seven features from the studied images. The features were the generalized dimensions D0, D1, D2, α at the maximum f(α) singularity spectrum, the spectrum width, the spectrum symmetrical shift point and lacunarity. The ANN obtains a single value decision with classification accuracy 97.78%, with minimum sensitivity 96.67%. Conclusion: Early stages of DR could be noninvasively detected using high-resolution OCTA images that were analysed by multifractal geometry parameterization and implemented by the sophisticated artificial neural network with classification accuracy 96.67%. This approach could promote risk stratification for the decision of early diagnosis of diabetic retinopathy.