An Effective Approach Based on Improved Convolutionary Neural Network Classifier for the Detection of Diabetic Retinopathy

Abstract

In diabetic individuals, diabetic retinopathy (DR) causes blindness. Therefore, detecting diabetic retinopathy at an early stage decreases vision loss. An successful approach for diabetic retinopathy prediction is discussed in this article. In the beginning, the input pictures of human retinal fundus images are preprocessed using histogram equalisation followed by Gabor filtering to reduce noise for enhancement. Then, using the Watershed method, segmentation is performed, and the features are retrieved through feature extraction. The best optimum features are selected using PCA (principal component analysis) approach. The morphological based post processing scheme was employed to further enhance the quality of selected features. At last, the classification approach is carried with the utilization of Google NET CNN classifier to classify/predict the retinal image as normal, abnormal, and severe. Google NET CNN has been developed with limited preprocessing step to distinguish visual features directly from image pixels. The findings are then evaluated and the efficacy of the new method is contrasted with other current methods. The quantitative findings were evaluated for Accuracy, precision, reliability, positive predictive levels and false predictive levels in parameters and were seen to deliver better results than current techniques.