# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 1 Introduction  
Diabetes is a disease in which blood sugar levels are too high. Victims are at the risk of developing different eye diseases i.e., diabetic retinopathy (DR) [1], diabetic macular edema (DME) [2], and glaucoma [3] that may result in a form of complete vision loss. DR is a disease that damages the retina, and blurred vision, floaters, and sudden vision loss are the main DR symptoms. Hemorrhages, microaneurysms, hard and soft exudates are the abnormality signs [4] of DR.  
* Hard exudates are bright yellow-colored spots with a wavy appearance on the retina, which are formed because of the leakage of blood from vessels. * Soft exudates are white lesions on the retina that occur due to occlusion of the arteriole. * Hemorrhages develop due to blood leakage from damaged vessels and appear as dark red spots. * Microaneurysms developed due to distortions in the boundary of blood vessels and appear as small red dots on the retina. DME is another eye disease that occurs when a patient already suffers from DR and is a cause of vision loss. Additional medical conditions resulting from poor blood sugar control increase the risk of blindness for people with DME that can occur at any phase of DR, although it is more likely to occur later as the disease progresses. DME leads to an accumulation of fluid in the macula region resulting in swelling of the macula, which is the central part of the retina that is dedicated to central vision. Loss of central vision occurs due to any damage in the macula region. Blurry vision, double vision, floaters and blindness are the common symptoms of DME if untreated [5]. Glaucoma is an eye disease that damages the optic disk (OD) and optic cup (OC) and causes vision loss in advanced stages, calculated to affect approximately 80 million people around the world [6; 7]. Some basic structural glaucoma signs are disc size, cup to disc ratio (CDR), the ratio of neuroretina rim in the inferior, superior, nasal and temporal quadrants, and peripapillary atrophy etc. are typically focused on the OD. OD is the morphological structure seen in the cross-sectional view of the optic nerve linking to the retina, and OC is the central part of OD. Glaucoma damages the optic nerve due to the imbalance in intraocular pressure (IOP) inside the eye. The affected nerve fibers deteriorates the retinal layer and increases the CDR and OD [8]. IOP causes injury to the nerve fibers comprising the optic nerve, and the OD initiates formation of a crater like-hole in front of the optic nerve head. Glaucoma causes the boundary of the disc to enlarge and its color changes from pink to pale. Eye diseases are normally diagnosed by measuring IOP, obtaining a patient's medical history, performing visual field loss tests accompanied by visual evaluation of disease through ophthalmoscopy to analyze the color, size, and shape of the optic nerve [8]. Therefore, segmentation of the affected region is not only beneficial for further intensive clinical evaluation by experts but also effective for preparing a computer-based automatic procedure for classification and is further robust to localization errors [9]. Initially, ophthalmologists diagnose eye diseases through visual checkup of DR lesions, OD, by determining the CDR, diameter, and ratio of disc area, and border irregularity. However, because of the limited number of available ophthalmologists, early disease diagnosis is usually delayed [10], when the timely diagnosis and cure of disease have the potential to avoid vision loss. To deal with these shortcomings, research is aiming for automated glaucoma detection through computer-aided diagnosis (CAD)-based solutions. For automated detection of eye diseases, handcrafted features have been used to differentiate between affected and normal regions of the images [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25].