## 1 Introduction
[21] compared a deep learning algorithm with manual grading, showing that the deep method either equals or exceeds manual grading on an Indian fundus dataset. On the other hand, reports emphasize the OCT's high optical capabilities and high DR diagnostic accuracy, which are independent of depth or spatial resolution [11]. OCT has been repeatedly used for DR detection and diagnosis. For example, [PERSON] et al. [22] presented a computer aided diagnostic (CAD) system for DR diagnostic and grading using OCT images. The system segmented the retina into 12 layers. Then, they estimated the reflectivity, curvature, and thickness of each layer. These features were fed to a neural network that distinguished between normal retinas and those with nonproliferative DR. Further, the system graded the level of the DR. [PERSON] et al. [23] used two deep networks for the early detection of DR from OCT images. One network was used to extract features from the original OCT images, and the other network was used to extract features from OCT retinal layer segmentation. A classification block was used to integrate the features extracted from both networks. [PERSON] et al. [24] segmented the OCT image into 12 layers. Then, they extracted the reflectivity, curvature, and thickness features from each layer. A deep network is trained on the cumulative probability distribution function (CDF) of the extracted features. Experiments, run on 40 training and 12 test OCT scans, achieved a detection accuracy of 92%. [PERSON] et al. [25] trained a classifier for automated DR detection from OCT-angiography (OCTA) based on three types of features: blood vessel density, blood vessel calibre, and the size of foveal avascular zone. Their system achievedan accuracy of 94.3%. More recently, [PERSON] et al. [26] extracted three features from each OCT layer: reflectivity, thickness, and curvature. Their developed system extracted four features from OCT angiography (OCTA): blood vessel caliber, vessel density, the size of the foveal avascular zone, and the number of bifurcation and crossover points. A random forest classifier is used for the diagnosis and grading of DR based on the combined features from OCT and OCTA. [PERSON] et al. [27] used 120 OCTA images and trained a supported vector machine on six extracted features: blood vessel density, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, foveal avascular zone area, and foveal avascular zone contour irregularity. Combined feature classification achieved an accuracy of 94.41% for control vs. DR and an accuracy of 92.96% for control vs. mild NPDR stage. The main disadvantages of the previous work are as follows:  
* Some studies contain a low number of test subjects;
* Traditionally, the input to the CNN is the original image, which may not be sufficient to report a high accuracy score;
* The overall accuracy can be improved. The main motivation behind this work is to achieve better accuracy by importing important features related to the disease. Unlike the work in the literature, the current study presents a novel fusion of features. Two medical markers are taken into account: the reflectivity and thickness features. Each of these features is represented by an input channel to a deep convolutional neural network (CNN). Three channels representing the high-level information (thickness and reflectivity) and low-level information (grey level image) are input to the CNN network. The main contributions/features of the proposed work are as follows:  
* The novel representation of the three input channels of the CNN network to integrate local (high-level) and global (low-level) information of the OCT image;
* The overall accuracy is improved compared to the related work. The rest of this paper is organized as follows. Section 2 represents the materials and methods. Section 3 illustrates the experimentation and their discussions. Section 4 is the discussion. Section 5 concludes the paper.