MULTI-SCALE ATTENTION FOR DIABETIC RETINOPA-THY DETECTION IN RETINAL FUNDUS PHOTOGRAPHS

Abstract

The diagnosis and/or grading of diabetic retinopathy (DR) in the retina fundus has traditionally been done by physicians using manual procedures. However, there has been a significant demand for automated eye diagnostic and grading systems due to the constant rise in the number of persons with diabetes over the past few decades. An excellent diagnostic and predictive value for treatment planning exists with automatic DR grading based on retinal fundus pictures. With the majority of the current automated DR grading systems, it is exceedingly challenging to capture significant features because of the minor changes between severity levels. This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures. This paper presents a deep learning-based method for automatically assessing diabetic retinopathy in retina fundus pictures. In order to increase the discriminative ability of the retrieved features, we implement a multi-scale attention mechanism within a deep convolutional neural network architecture in this research. Additionally, we provide a brand-new loss function termed modified grading loss that enhances the training convergence of the suggested strategy by taking into account the distance between various grades of distinct DR categories. The suggested technique is trained, validated, and tested using a dataset about diabetic retinopathy that is openly available. The experimental findings are presented to illustrate how well the suggested strategy competes.

1. INTRODUCTION

Diabetic Retinopathy(DR) is a disorder caused by excessive blood sugar levels that damages the rear of the eye (retina). It is a long-term microvascular issue brought on by uncontrolled diabetes mellitus (DM) and is one of the most significant consequences of type 2 diabetes (T2DM) Ali et al. (2016); Naserrudin et al. (2022) . Diabetic retinopathy is classified into four types: no visible diabetic retinopathy (No DR), non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and advanced diabetic eye disease (ADED). The multiple DR severity levels are broken down in Figure 1 . Physicians frequently use the international clinical DR severity scale developed by the American Academy of Ophthalmology (AAO) to classify patients as having non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), or maculopathy Ngah et al. (2020) . It defined NPDR as the presence of any of the following disorders with no signs of proliferative retinopathy: micro-aneurysms, intra retinal hemorrhage, venous beading, or intra retinal microvascular abnormalities (IRMAs). Neovascularization, vitreous or pre-retinal hemorrhage, or both, were used to describe PDR. Following that, fundus images were labeled as having no DR, NPDR, PDR, advanced diabetic retinopathy (ADED), cataract, maculopathy, or glaucoma suspicious Ngah et al. (2020); Mallika et al. (2011) . This classification assists in deciding when a referral is necessary, how frequently to monitor/screen patients, how to treat them, and other factors Saxena et al. (2020) . Manual eye screening for DR entails identifying this eye disorder through visual examination of the fundus, either through direct inspection (in-person dilated eye examinations) or through analysis of digital color fundus photographs of the retina. According to a number of studies (see e.g., Liesenfeld et al. (2000) ; Olson et al. (2003) ; Gangaputra et al. ( 2013)), fundus photography telemedicine has equivalent sensitivity and specificity as in-person screening for DR. Additionally, patients enjoy

