Abstract
Patients with diabetes are at risk of developing a retinal disorder called Proliferative Diabetic Retinopathy (PDR). One of the main characteristics of PDR is the development of neovascularization, a condition in which abnormal blood vessels are formed on the retina. This condition can cause blindness if it is not detected and treated early. Numerous studies have proposed different image processing techniques for detecting neovascularization in fundus images. However, because of its random growth pattern and small size, neovascularization remains challenging to detect. Hence, deep learning techniques are becoming more prevalent in neovascularization identification because of their ability to perform automatic feature extraction on objects with complex features. In this paper, a method of neovascularization detection based on transfer learning is proposed. The performance of the transfer learning method is investigated using four pre-trained Convolutional Neural Network (CNN) models, which include AlexNet, GoogLeNet, ResNet18, and ResNet50. In addition, an improved network based on the combination of ResNet18 and GoogLeNet is proposed. Evaluation on 1174 retinal image patches showed that the proposed network could achieve 91.57%, 85.69%, 97.44%, and 97.10% of accuracy, sensitivity, specificity, and precision, respectively. We demonstrated that the proposed method outperforms each individual CNN for neovascularization detection. It also shows better performance compared to another method that utilized deep learning models for feature extraction and Support Vector Machine (SVM) for classification.
Highlights
Diabetic Retinopathy (DR) is more prevalent in patients with long-term diabetes [1]
The area under the ROC curve (AUC) of AlexNet, GoogLeNet, ResNet18, ResNet50, and the proposed network are 0.8737, 0.8864, 0.9685, 0.9345, and 0.9855, respectively. These results show that the proposed network with the biggest AUC is the best network for classifying Neo and not contain neovascularization (NotNeo) image patches
Experiment results based on the performance metrics and Receiver Operating Characteristic (ROC) plots show that the proposed network outperformed all these networks
Summary
Diabetic Retinopathy (DR) is more prevalent in patients with long-term diabetes [1]. It is categorized into Nonproliferative DR (NPDR) and Proliferative DR (PDR). The newly formed vessels are delicate and can burst, resulting in retinal bleeding If these new blood vessels are formed within the diameter of the optic disk, the condition is referred to as neovascularization at the optic disk (NVD). Neovascularization elsewhere (NVE) refers to the new vessels forming one disk diameter away from the optic disk Both NVD and NVE are blamed for vessel growth and vitreous hemorrhage, resulting in visual loss. PDR must be detected early to preserve the patient’s vision This can be accomplished by analyzing the patient’s fundus image to detect blood vessels and identifying the newly formed vascular associated with neovascularization. We demonstrated that the proposed network (ResNet18 + GoogLeNet combination) could outperform other pre-trained networks in detecting neovascularization through transfer learning.
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