Diabetic Retinopathy Classification Using Hybrid Color-Based CLAHE and Blood Vessel in Deep Convolution Neural Network

The most widespread illness of the diabetic eye that causes missing eye vision is diabetic retinopathy (DR), which requires disclosure soon to prevent the vision loss of the sick. In this study, two features are extracted from retina images with for multiclass DR classification, which include color-based Blood Vessel (BV) segmentation and color-based Contrast-Limited-Adaptive-Histogram-Equalization-Top-Hat (CLAH-TH) segmentation. These features are integrated to enhance the accuracy of classification and detection of DR. Variant models, especially VGG19 and InceptionV3, are trained using a transfer learning approach on the proposed extracted features for DR grading. The data augmentation strategy is employed to improve the accuracy and performance of the proposed method by balancing the dataset and aligning the number of images in each class. Experimental results demonstrate that the proposed method outperforms contemporary CNN models when utilizing the suggested features. The best results obtained from experiments on the Kaggle DR database using the pretrained VGG19 model include an accuracy of 96.7%, a sensitivity of 0.971, and a specificity of 0.981.