## 1 Introduction  
Alzheimer's disease (AD) is by far the most common type of dementia, generally seen in elderly people. For the majority of cases, AD symptoms begin to appear during the mid-60s, although the early-onset may occur in ages as early as the 30s. It is estimated that around 106 million people will be diagnosed in the world by 2050 due to the increase in the aging population [1]. During the progression of AD, the brain structure changes due to the deposition of amyloid-\(\beta\) (A\(\beta\)) plaques and hyperphosphorylated tau. The initial damage starts at the hippocampus [2], which handles episodic and spatial memory as well as working as a relay between the brain and the rest of the body, so the damage disrupts these pathways. The symptoms of AD are the result of these A\(\beta\) plaques and intracellular neurofibrillary tangles [3,4]. This decreases the brain metabolism of both glucose and oxygen, leading to progressive memory loss and inability to move in the late stages [5]. These changes start to form years before the initial clinical symptoms of AD are seen. Mild cognitive impairment (MCI) causes cognitive changes to the person that is noticeable by the family members and friends, but the person may still carry out daily activities. It's been estimated that 15% to 20% of people over the age of 65 have MCI [6]. Not every case progresses into dementia; in some individuals, MCI remains stable throughout their lives [7]. The stages of AD progress can be seen as low-intensity brain cell activity in medical images. Positron emission tomography (PET) is an imaging technique that uses radiotracers to measure the metabolic processes, and magnetic resonance imaging (MRI) is another imaging technique that uses strong magnetic fields, magnetic field gradients, and radio waves. Resting-state functional magnetic resonance imaging (fMRI) is also considered a promising biomarker; however, in more severe cases, may cause problems due to the fMRI being very sensitive to head motion, mitigating its usefulness [8]. PET imaging provides substantially sensitive assays, easily detecting a very low concentration of molecules of interest labeled through positron emitters, and MRI suffers from very low molar sensitivity for different metabolites and probes. This, coupled with the absolute quantification of substrate concentration being more challenging with MRI, PET imaging is generally chosen for AD imaging [9]. Different types of nuclear tracers are developed for various purposes to highlight different parts of the body; some of the radiotracers that are used to detect A\(\beta\) plaques in the brain are florbetapir 18F, flutmetamol F18, PiB, and florbetaben 18F [10]. Unfortunately, at the time of writing this paper, there is no definite cure for AD, and most of the treatments aim at alleviating the disease-related symptoms. Beginning from the early 1970s, multiple computer-aided diagnostics (CAD) systems were proposed. Their early versions were usually based on manually extracted feature vectors [11]. Later, these vectors were trained in a supervised method with a machine-learning model such as a support vector machine (SVM) [12] to classify the input vector. It was soon realized that there were many shortcomings to such systems [13]. Researchers turned towards data mining approaches in the 1980 and 1990s to develop more robust and flexible systems to overcome these limitations. With the improvements to imaging methodologies, machine learning methodologies have been proposed to detect AD. These studies generally have multiple steps: feature extraction, feature selection, dimensionality reduction, and feature-based classification algorithm selection [14]. The main issue with such works has been the reproducibility of these approaches [15].