**1. Introduction**

Alzheimer's disease is a progressive disease of the mental faculties commonly seen in the elderly. Significant symptoms of this disease are memory loss, judgment, and important behavioral changes in the person [1]. The disease results in the loss of synapses of neurons in some areas of the brain, necrosis of brain cells in different areas of the nervous system, the formation of spherical protein structures called aging plaques outside neurons in some areas of the brain, and fibrous protein structures called neurofibrillary Tangles. A spiral is identified in the cell body of neurons. There is currently no definitive diagnosis or treatment for this disease. The prevalence of Alzheimer's disease is increasing rapidly [2]. The number of Alzheimer's patients in Iran has almost doubled in 13 years, according to the Iranian Alzheimer's Association. On the other hand, the costs of treatment, as well as care and nursing of these patients, are very high and difficult. This disease causes various mental disorders in the patient. It usually takes several years from the first signs of the disease to the acute stages of the disease, when most of the brain cells are destroyed. If this disease is not detected in time, new and up-to-date treatment methods will not work. The solution is to accurately identify the mechanism of this disease and its effect on brain signals,

which is very difficult due to the dynamic nature of EEG signals and medical images, which due to the complex nature of this disease as a result, we must determine the best and most effective indicator to identify this disease and how this indicator relates to the characteristics of the brain signal and medical images. Medical image analysis has become very important in the diagnosis of mild Alzheimer's disease in recent years [3]. The high volume and complexity of medical images make early detection of Alzheimer's disease difficult for physicians and increase the workload of radiologists, in which case the use of computer-aided diagnostic (CAD), including image processing technologies, can help to increase the accuracy of diagnosis. The use of machine learning systems and deep processing of medical images with proper labeling and feature extraction can be one of the effective methods of diagnosing this disease [4]. Deep learning methods and machine learning techniques can be two effective and accurate methods in the early diagnosis of Alzheimer's disease [5]. Hippocampal volume analysis is used in medical image processing to diagnose mild Alzheimer's disease. Because before the atrophy creation, analyzing the volume of hippocampal material in MRI images can be used with deep processing techniques to extract proper features to identify mild-Alzheimer's disease [6]. 3D segmentation of MRI images further helps researchers diagnose Alzheimer's disease and obtain important information [7]. Determining the degree of atrophy of MRI images is an effective method for early detection of Alzheimer's disease. Also, assessing the degree of asymmetry in both the right and left hemispheres and analyzing volumetric mismatch can differentiate from mild to severe Alzheimer's disease [7]. Using statistical features of signal and obtaining temporal information and using spatial features of MRI images is an effective method for the more accurate evaluation of Alzheimer's disease [8]. Cortical atrophy means the gradual destruction of the nerve cells that make up the upper regions of the brain, specifically the structures found in the cerebral cortex, mostly due to a reduction or loss of oxygen and nutrients in these areas. There are also different methods for evaluating the medial temporal lobe that has different functional accuracy [9]. Longitudinal T1-weighted MRI studies are another effective way to distinguish mild-Alzheimer's patients from healthy ones [10]. Also, extracting the appropriate characteristics and deciding on the classification in this field are among the issues to be considered. Currently, there are several methods to diagnose this disease, and it is important to examine two issues in these methods. The cost of these methods and the acceptable accuracy are the points under consideration, so it seems necessary to identify a low-cost method with appropriate accuracy and precision. Therefore, in addition to extracting proper features of EEG signals and MRI images for AD diagnosis, another aim of this study is to identify the proper multimodal combining of these extracted features to increase the accuracy of mild-Alzheimer's disease detection by using a proper classifier.
