## 1 Introduction  
Alzheimer's disease (AD) is a brain disease that results in the loss of cognitive functions. Aggregation of protein amyloid and tau in the brain is considered a distinguishing characteristic of AD [1]. Its symptoms include problems with memory such as difficulties recalling recent dialogues, issues with communication, and impaired decision-making [1]. At an advanced level, daily activities such as walking and eating are also affected by AD [1]. It is estimated that after AD diagnosis, patients live around 5.7 years on average [2]. The World Alzheimer's Report 2018 calculated that the overall cost due to Alzheimer's disease is USD 1 trillion per year. This encompasses costs such as nursing and hospital care. The total number of people with AD was estimated to be 416 million [3]. This number is much greater than estimates in other studies because it includes people with preclinical AD in the calculation. Overall, these statistics show the significance of the problems that have arisen due to AD. There is evidence suggesting that brain changes due to AD begin 20 years before the first clinical symptoms [4]. This shows the possibility of early detection of AD before the start of first symptoms. Early discovery of AD in patients, before the onset of symptoms, can allow for preventive measures that can alleviate AD-related problems. In a recent review, [PERSON] et al. [5] identified 10 factors such as cognitive activity and stress that have strong evidence of their effectiveness in AD intervention. Furthermore, it has been estimated that a 10-25% decrease in AD risk factors such as smoking and obesity might even reduce 1.1-3 million AD cases globally [6]. These studies provide a lot of motivation for discovering biomarkers for early detection of AD. Since AD is a global problem affecting millions of people, affordable and easy methods for screening AD are needed. The retina is a potential option for achieving this goal. This is because the retina is considered a visible extension of the central nervous system (CNS) with regard to embryology and anatomy [7; 8]. The retina's ganglion cells exhibit similar characteristics to CNS neurons, and it's axons constitute the optic nerve [9]. Various neurodegenerative diseases exhibit symptoms in the retina [7; 10]. There are studies showing that retina is affected in patients with AD [11]. For example, decreased retinal nerve fiber layer (RNFL) thickness in patients with AD and MCI has been reported [12]. Also, there are studies showing the presence of amyloid plaques in the retina of individuals with AD [13]. These studies illustrate the relationship between the brain and retina, making the retina a potential biomarker for AD detection. Images of the retina are easy to acquire. For example, optical coherence tomography (OCT) machines are available in many ophthalmologic centers. OCT is a non-invasive imaging modality that generates cross-sectional images and can be used to visualize retinal layers [14]. Moreover, fundus images can be acquired even with a phone camera. In the past, AI techniques have shown good performance in the detection of various ocular diseases from retinal images such as diabetic retinopathy [15] and glaucoma [16; 17]. Moreover, retinal images have also been used to predict other diseases such as kidney disease [18]. However, the development of AI techniques for AD detection from retinal images is still in progress. Various techniques based on machine learning algorithms have been developed over the past 5-10 years. For example, [PERSON] et al. [19] trained the support vector machine (SVM) algorithm from fundus images to distinguish between healthy and AD individuals. Similarly, [PERSON] et al. [20] proposed a technique based on the XGBoost algorithm to identify AD patients by using features extracted from OCT images. Another type of AD detection method is based on deep learning. For instance, in a collaborative study, a DL model to screen AD patients was built by utilizing fundus images [21].