Abstract
Retinal vessel images give a wide range of the abnormal pixels of patients. Therefore, classifying the diseases depending on fundus images is a popular approach. This paper proposes a new method to classify diabetic retinopathy in retinal blood vessel images based on curvelet saliency for segmentation. Our approach includes three periods: pre-processing of the quality of input images, calculating the saliency map based on curvelet coefficients, and classifying VGG16. To evaluate the results of the proposed method STARE and HRF datasets are used for testing with the Jaccard Index. The accuracy of the proposed method is about 98.42% and 97.96% with STARE and HRF datasets respectively.
Highlights
Diabetes is a condition that occurs when the pancreas does not produce enough insulin or when the body loses its ability to metabolize insulin
This paper proposes a method for diabetic retinopathy classification based on the improvement of the second general wavelet transform with the VGG-16 Convolutional Neural Network (CNN)
Any disease prediction or classification system must adapt to the medical images
Summary
Diabetes is a condition that occurs when the pancreas does not produce enough insulin or when the body loses its ability to metabolize insulin. Authors in [19] proposed a method for diabetic retinopathy detection based on U-Net and ResNet-18. In their experiments, they assessed two segmentation images. Authors in [20] proposed a method for retina image recognition This method includes two phases: finding features for healthy retinal image recognition and using vascular and lesion-based features for diabetic retinopathy retinal image recognition. This paper proposes a method for diabetic retinopathy classification based on the improvement of the second general wavelet transform with the VGG-16 CNN. The VGG16 model classifies the images as diabetic or not
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