Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images

Abstract

Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method. Although deep learning models provide automatic feature extraction and classification, training such models from scratch requires a larger annotated dataset. The availability of annotated training datasets is considered a core issue for implementing deep learning in the classification of medical images. The models based on transfer learning are widely adopted by the researchers to overcome annotated data insufficiency problems and computational overhead. In the proposed study, features are extracted from fundus images using the pre-trained network VGGNet and combined with the concept of transfer learning to improve classification performance. To deal with data insufficiency and unbalancing problems, we employed various data augmentation operations differently on each grade of DR. The results of the experiment indicate that the proposed framework (which is evaluated on the benchmark dataset) outperformed advanced methods in terms of accurateness. Our technique, in combination with handcrafted features, could be used to improve classification accuracy.

Keywords: annotated data insufficiency; computer-aided diagnosis; convolutional neural network; diabetic retinopathy; fundus images; transfer learning.

Figure 1

These images show the different types of retinopathies in the fundus images. (a) Normal, (b) mild, (c) moderate, (d) severe, and (e) proliferative.

Figure 2

Proposed framework for detection and classification of diabetic retinopathy. In the first phase, retinal images are preprocessed, and data augmentation operations are performed individually on each grade of DR to improve classification accuracy. In the last, model-based on transfer learning is used for automatic features extraction and classification of DR into different stages.

Figure 3

Dataset distribution over DR severity. There were about 73% of images in the normal category, while only 2% of them from the proliferative DR category. Thus, it was an imbalanced dataset with 36:1 for normal and proliferative DR.

Figure 4

The preprocessed retinal images after applying contrast limited adaptive histogram equalization to adjust contrast in images. (a,c) Original retinal fundus images, (b,d) preprocessed images.

Figure 5

Some examples of adding weighted Gaussian blur to the retinal images, which is employed to reduce noise and increase image structure. First row are original fundus images, and second images are output of the preprocessed images.

Figure 6

The visual exemplification of some augmentation operations performed on preprocessed images to augment the retinal dataset (a) Original image (b) Cropping (c) Shearing (d) Flipping (e) Rotating (f) Zooming (g) Translating (h) All augmentation.

Figure 7

(a) Shows the training and validation accuracy of the proposed framework and (b) shows the training and validation loss of the proposed fine-tuned VGGNet framework.

Figure 8

The receiver operator characteristics curve of the proposed framework.