A Comprehensive Analysis of Diabetic Retinopathy Detection in Retinal Fundus Images Using Different Convolutional Neural Network
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Abstract
Diabetic Retinopathy (DR) harms the retinal tissue's blood vessels, causing them to leak fluid and results in permanent vision loss. Therefore, rigorous discussion and evaluation of the associated procedures and findings are necessary for DR detection. In this work, the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset and the High Resolution Fundus (HRF) datasets are utilized. The Contrast Limited Adaptive Histogram Equalization is employed in the preprocessing stage to enhance the quality of the fundus images. There are various popular edge detection algorithms utilized here like Robert, Sobel, Prewitt and Canny edge detector but our experimental findings have shown that the Canny edge detector performs better than its other counterparts. So, Canny has been used for the Segmentation of fundus images. Finally, the pretrained Convolutional Neural Networks namely MobileNet, InceptionV3, ResNet- 50, VGG16 and VGG19 are proposed to detect DR in retinal fundus images. To evaluate the effectiveness of the proposed model, both datasets are divided into two parts, where one part is utilized for training the proposed model and another part is utilized for testing the proposed model. The performances of different techniques are evaluated based on the standard performance parameters like Sensitivity, Specificity, Precision, Accuracy and Receiver Operating Characteristic Curve etc.
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