Diabetic retinopathy detection using adaptive deep convolutional neural networks on fundus images

Abstract

Diabetic retinopathy (DR) is an age-related macular degeneration eye disease problem that causes pathological changes in the retinal neural and vascular system. Recently, fundus imaging is a popular technology and widely used for clinical diagnosis, diabetic retinopathy, etc. It is evident from the literature that image quality changes due to uneven illumination, pigmentation level effect, and camera sensitivity affect clinical performance, particularly in automated image analysis systems. In addition, low-quality retinal images make the subsequent precise segmentation a challenging task for the computer diagnosis of retinal images. Thus, in order to solve this issue, herein, we proposed an adaptive enhancement-based Deep Convolutional Neural Network (DCNN) model for diabetic retinopathy (DR). In our proposed model, we used an adaptive gamma enhancement matrix to optimize the color channels and contrast standardization used in images. The proposed model integrates quantile-based histogram equalization to expand the perceptibility of the fundus image. Our proposed model provides a remarkable improvement in fundus color images and can be used particularly for low-contrast quality images. We performed several experiments, and the efficiency is evaluated using a large public dataset named Messidor's. Our proposed model efficiently classifies a distinct group of retinal images. The average assessment score for the original and enhanced images is 0.1942 (standard deviation: 0.0799), Peak Signal-to-Noise Ratio (PSNR) 28.79, and Structural Similarity Index (SSIM) 0.71. The best classification accuracy is [Formula: see text], indicating that Convolutional Neural Networks (CNNs) and transfer learning are superior to traditional methods. The results show that the proposed model increases the contrast of a particular color image without altering its structural information.

Keywords: Artificial intelligence; Deep learning and Health; Machine learning; Medical imaging.

Fig. 1

Block diagram of the proposed method for fundus color image enhancement.

Fig. 2

Fundus images dataset.

Fig. 3

(C) Cover fundus image; , and are intensity and contrast enhanced image; Selected areas in the image frame with zoomed in the bottom row.

Fig. 4

Enhancement outcomes of lowest luminosity images of proposed method, (C) Cover fundus image; , , , , and are proposed different intensity and contrast value of enhanced image.

Fig. 5

Enhancement outcomes of lighest luminosity images of proposed method, (C) Cover fundus image; , , , , and are proposed different intensity and contrast value of enhanced image.

Fig. 6

Enhanced processed images. The blurred image vessel tracking and after segmentation of enhance image.

Fig. 7

(a) Classification, specificity, sensitivity and PSNR outcome of original images (b) Classification, Sensitivity, Specificity and PSNR performance outcome of enhanced image. Lowest and higher performance outcome (Accuracy: 91.01–96.94) for the ratio of PSNR (28.60–29.17).