Automatic Diabetic Retinopathy grading system using U-net and ANN

Automatic Diabetic Retinopathy grading system using U-net and ANN

Peush Paul S ^1,* ,Prof.J M Aravind², Dr.V Suresh³

¹ ME Student, Department of ECE, Mar Ephraem College of Engineering and Technology, Elavuvilai

² Professor, Department of ECE, Mar Ephraem College of Engineering and Technology, Elavuvilai

³ Head of Department, Department of ECE, Mar Ephraem College of Engineering and Technology, Elavuvilai

Abstract— Multi-label classification (MLC) is considered an essential research subject in the computer vision field, principally in medical image analysis. For this merit, we derive benefits from MLC to diagnose multiple grades of diabetic retinopathy (DR) from various colored fundus images, especially from multi-label (ML) datasets. Therefore, ophthalmologists can detect early signs of DR as well as various grades to initiate appropriate treatment and avoid DR complications. In this paper, we propose a comprehensive ML computer-aided diagnosis (CAD) system based on deep learning technique. First, we eliminate noise, enhance quality, and standardize the sizes of the retinal images. Second, we differentiated between the healthy and DR cases by calculating the gray level run length matrix average in four different directions. The system automatically extracts the four changes: exudates, microaneurysms, hemorrhages, and blood vessels by utilizing a deep learning technique (U-Net). Furthermore, multi-oriented gradient weighting is combined with U-Net to achieve accurate segmentation. Next, we extract six features, which are the gray level co-occurrence matrix, areas of the four segmenting pathology variations, and the bifurcation points count of the blood vessels. Finally, the resulting features were afforded to an Artificial Neural Network (ANN) based on a classifier chain to differentiate the various DR grades. We utilized eight benchmark datasets (four of them are considered ML) and six different performance evaluation metrics to evaluate the proposed system’s performance. It achieved 95.1%, 91.9%, 86.1%, 86.8%, 84.7%, 86.2% for accuracy, area under the curve, sensitivity, specificity, positive predictive value, and dice similarity coefficient, respectively. The experiments show encouraging results as compared with other systems.

       Keywords—: Multi-label computer-aided diagnosis (ML-CAD), multi-label classification (MLC), deep learning (DL), U-Net, diabetic retinopathy (DR).