## 1 Introduction  
The term \"telemedicine\" was defined in the 1970s by [PERSON] and [PERSON] as \"healing at a distance\". The World Health Organization (WHO) presented a standard meaning of telemedicine as \"the delivery of health care services, where distance becomes a critical factor, by every healthcare expert by making use of information and communication technology (ICT) for interchanging validated data for prognosis, medication and preventing injuries and disease\" [1]. Telemedicine depends on ICT, termed a \"different set of technical tools and sources utilized for creating, transmitting, exchanging or sharing, storing, information [2]. Such resources and technological tools involve live broadcasting technology (television, webcasting, and radio), recorded broadcasting technology (podcasting, video and audio players, and memory gadgets), telephony (mobile or fixed, satellite, video-conferencing), and the internet (emails, websites, and blogs)\". A creative grouping of screening with the help of Optical Coherence Tomography (OCT), fundus cameras, and other gadgets with telemedicine escorted in the period of teleophthalmology can be implemented in non-eye care backgrounds and in ophthalmology offices, which include primary care workplaces [3]. This can be possible by appropriate follow-up eye care and remote grading. Developing global interest in the usage of telemedicine in screening for diabetic retinopathy (DR) caused the emergence of several journals during the past few years [4].  
Diabetes mellitus was a very expensive and pandemic chronic ailment. It affects nearly 4.15 billion people across the world, responsible for 12% of the global health expenses, and even so, 1 in 2 people were untreated and undiagnosed [5]. Subsequently, life-threatening complexities because of diabetes mellitus, namely cardiomyopathy, neuropathy, strokes, retinopathy, and nephropathy have hiked across many countries. Today, caregivers and patients will stay on a query relating to diabetes management [6]. For instance, frequent diagnoses and offering essential advice for patients in self-management were needed for the prevention of acute terrible complexities and to reduce the danger of life-long conditions [7]. The surge of a higher volume of real-world data collected at the time of medications has generated incredible enthusiasm in diabetic care. Among them, imagery reports present a great effect on developing new insights and disturb the present understanding of diabetic care. Currently, medical imaging is mostly utilized for diagnosing, prioritizing treatment, and evaluating replies to medications in modern medicine [8]. The main reason was the work pressure of a health care professional surges significantly because of a huge number of patients indulging in population screening and thus, patients are waiting in a lengthy queue [9]. Artificial intelligence (AI) was increasingly automating health care practices and provides high precision, satisfaction, and efficiency [10]. With the recent progression in digitalized data acquisition, machine learning (ML), and computer vision (CV), AI was diffusing into medical decision-making processes that are formerly scrutinized below the direct supervising of human professionals.  
This study develops an XAI-enabled teleophthalmology for diabetic retinopathy grading and classification (XAITO-DRGC) model. The proposed XAITO-DRGC model uses median filtering (MF) and contrast enhancement as a pre-processing step. In addition, the XAITO-DRGC model applies U-Net-based image segmentation and SqueezeNet-based feature extractor. Moreover, Archimedes optimization algorithm (AOA) with a bidirectional gated recurrent convolutional unit (BGRCU) is exploited for DR detection and classification. The experimental validation of the XAITO-DRGC model is tested using a benchmark dataset and the results are assessed under distinct aspects.