Robust classification of neovascularization using random forest classifier via convoluted vascular network. (April 2021) - P, Geetha Pavani; Biswal, Birendra; Biswal, P.K.
Highlights: Neovascularization is an alarming phase of Proliferative Diabetic Retinopathy (PDR) that leads to visual impairment of the human eye. This research article proposed a novel method for accurate vessel segmentation using 2-dimensional Gabor Wavelets and matrix convolution. The major contribution includes the formulation enhancing the robustness of the classifier by a hybrid approach considering two well-established approaches. The matrix convolution method is incorporated to preserve the shape of the vascular map and to reduce the impact of unwanted noise on the segmented vascular map. Thirty-six pertinent features are extracted from the enhanced vascular map and fed to the Random Forest (RF) classifier as an input for the effective classification of retinal images into Healthy, NVD, and NVE images. The efficacy of the proposed method is estimated on globally accessible databases by calculating the accuracy and Out of Bag (OOB) error. The performance of the RF classifier is compared with Quadratic Support Vector Machine (Q-SVM), Online Sequential Extreme Learning Machine (OS-ELM) & Mediods classifiers. Abstract: Diabetic Retinopathy is a severe visual disorder in the retina, which causes permanent blindness due to prolonged hyperglycemia. This paper primarily focuses on the classification of Neovascularization into Neovascularization on Disc (NVD) and Neovascularization Elsewhere (NVE). Neovascularization is an alarming phase of Proliferative Diabetic Retinopathy (PDR) that leads to visual impairment of the human eye. This research article proposes a novel method for accurate vessel segmentation using 2-dimensional Gabor Wavelets and matrix convolution. The matrix convolution method is incorporated to preserve the shape of the vascular map and to reduce the impact of unwanted noise on the segmented vascular map. Thirty-six pertinent features are extracted from the vascular map and fed to the Random Forest (RF) classifier as an input for the effective classification of retinal images into Healthy, NVD, and NVE images. The efficacy of the proposed method is estimated on globally accessible databases by calculating the accuracy and Out of Bag (OOB) error. The performance of the RF classifier is compared with Quadratic Support Vector Machine (Q-SVM), Online Sequential Extreme Learning Machine (OS-ELM) and Mediods classifiers. The combination of matrix convolution and feature selection with RF classifier outperforms other existing classifiers by achieving excellent results with an Out of Bag (OOB) error less than 0.05 and an average accuracy of 98 %.