Abstract
Diabetic Retinopathy (DR) is one of the main causes of blindness that can be overcome, if it is early detected. This work proposes an automated early detection and grading of DR using fundus images. The proposed detection and grading system investigate different deep learning architectures (i.e., ResNet and AlexNet) that are applied to an augment data to extract deep compact features of the fundus images. The extracted features are input to a pixel-wise Neural Network (NN) classifier or a Support Vector Machine (SVM) classifier for automated DR grading. The performance of the proposed system is evaluated using a publically available fundus Indian Diabetic Retinopathy Image Dataset (IDRiD), collected for ISBI-2018 challenge. The IDRiD dataset consists of 516 retinal images of normal and different DR grades, i.e., mild, moderate, severe, and Proliferative Diabetic Retinopathy (PDR). Our system achieves an overall accuracy of 95.73%, sensitivity of 95.73%, and specificity of 98.51% utilizing an AlexNet-based architecture and a pixel-wise NN classifier. Compared to the previous related work, the proposed system shows promising DR grading performance.
Highlights
DIABETIC Retinopathy (DR) is one of the main causes of visual blindness
For the task of automated Diabetic Retinopathy (DR) grading and/or related lesions detection, different methodologies have been developed throughout the literature
These methods can be categorized as traditional methods or deep learning methods [6]
Summary
DIABETIC Retinopathy (DR) is one of the main causes of visual blindness. According to the recent report of the Vision Loss Expert Group (VLEG) of the global burden of disease study, DR has increased the number of blind people, during the period from 1990 to 2015, from 0.2 million to 0.4 million [1]. For the task of automated DR grading and/or related lesions detection, different methodologies have been developed throughout the literature. These methods can be categorized as traditional methods or deep learning methods [6]. Marin et al [7] applied a system to detect the retinal exudate lesions based on feature extraction and supervised regression-based classification. Deep learning algorithms, based on Convolutional Neural Networks (CNN), were recently applied to retinal images, e.g., for lesion detection [9,10,11,12,13] and Received: (12 July, 2020) - Revised: (8 October, 2020) - Accepted: (11 October, 2020)
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