Diabetic retinopathies have to be detected early and treated to avoid serious damages to patients’ retina. A severe progress of diabetes can deteriorate human vision and the effects of a Proliferative Diabetic Retinopathy (PDR) could appear in fundus images, showing a neovascularization that can rise abruptly. Until now only some network models for classifying presence/absence of PDR have been faced by means of PNNs or SVMs. In this paper a first approach to follow diabetic patients affected by early PDR via a novel neural classifier based on a Fundus Image Preprocessing Subsystem and a Radial Basis Probabilistic Neural Network (RBPNN) is presented. The proposed classifier aims at classifying a certain number of diabetic patients by means of their accurately preprocessed digital fundus images and could support their follow-up paths in alerting if variations in retinal vasculature of classified PDR should occur.
On Classifying Diabetic Patients’ with Proliferative Retinopathies via a Radial Basis Probabilistic Neural Network / Carnimeo, Leonarda; Nitti, Rosamaria. - STAMPA. - 9227:(2015), pp. 115-126. [10.1007/978-3-319-22053-6_14]
On Classifying Diabetic Patients’ with Proliferative Retinopathies via a Radial Basis Probabilistic Neural Network
Carnimeo, Leonarda;
2015-01-01
Abstract
Diabetic retinopathies have to be detected early and treated to avoid serious damages to patients’ retina. A severe progress of diabetes can deteriorate human vision and the effects of a Proliferative Diabetic Retinopathy (PDR) could appear in fundus images, showing a neovascularization that can rise abruptly. Until now only some network models for classifying presence/absence of PDR have been faced by means of PNNs or SVMs. In this paper a first approach to follow diabetic patients affected by early PDR via a novel neural classifier based on a Fundus Image Preprocessing Subsystem and a Radial Basis Probabilistic Neural Network (RBPNN) is presented. The proposed classifier aims at classifying a certain number of diabetic patients by means of their accurately preprocessed digital fundus images and could support their follow-up paths in alerting if variations in retinal vasculature of classified PDR should occur.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.