## 1 Introduction  
Alzheimer's disease (AD) is a neurodegenerative disorder that causes progressive problems with memory, cognition, learning, and higher executive function [1] in older people. Mild cognitive impairment (MCI), as a traditional phase between normal aging and AD, involves a slight but noticeable decline in memory and thinking [2]. Generally, MCI patients who later convert to AD are considered to have progressive MCI (pMCI), whereas MCI patients who have stable cognitive conditions are considered to have stable MCI (sMCI). One important clinical task is to distinguish between sMCI and pMCI in order to identify MCI patients who are at high risk of converting to AD in the foreseeable future. Consequently, early detection of this group of patients may help clinicians to effectively treat the condition. To this end, different neuroimaging modalities such as magnetic resonance imaging (MRI) [3; 4], positron emission tomography (PET), and single-photon emission computed tomography (SPECT) [5; 6; 7; 8; 9]have been widely used in a variety of AD studies. In contrast to PET and SPECT modalities [10], MRI does not require injection of a radioactive pharmaceutical and also provides excellent spatial resolution with good contrast images. Radiological studies have confirmed that MRI measures can be used as a robust procedure to track brain atrophy due to neuronal loss [11]. In the case of AD, various MRI studies have shown that structural brain alterations due to AD can be used to distinguish AD patients from healthy controls (HCs) as well as pMCI patients from sMCI patients [12; 13]. Furthermore, a recent study has documented that volumetric measures achieved from MRI data have the potential ability of detecting the biological heterogeneity patterns among amnestic MCI patients [14]. Generally, in the case of feature extraction from MRI, the AD classification studies can be categorized into three main groups: (1) Vertex-wise metrics use cortical and subcortical features, such as volumetric, thickness, and area information. The performance of this method is highly reliant on surface registration [15]. (2) Region of interest (ROI)-based metrics use a nonlinear registration followed by a template to extract the features from each ROI [16]. Practically, the dimension of features in this method is a function of the number of ROIs considered. As a drawback, this method has difficulty in reflecting the minor abnormalities associated with brain diseases [17]. (3) Voxel-wise metrics use the voxel intensities extracted from MRI scans as a feature. Although the voxel-wise metric robustly reflects the minor changes occurring due to brain disease, this approach suffers from the curse of dimensionality, in which the number of extracted features (i.e., voxel intensities) is markedly higher than the number of samples. With respect to this point, a robust feature selection for high-dimensional space plays a crucial role in voxel-wise approaches. In the present study, we aimed to use a voxel-wise metric followed by advanced pattern recognition methods to develop a robust computer-aided diagnosis (CAD) system for AD classification. We introduce a merit-based feature selection (MFS) method as part of a proposed CAD framework to select the most informative features (i.e., voxel intensities) from high-dimensional space extracted from whole-brain analysis. We will show how, by using the proposed MFS, we are able to classify AD patients from healthy individuals and predict MCI conversion with robust performance. We additionally investigate the effect of different MRI spatial resolutions on the proposed CAD framework. The efficiency of the proposed CAD framework is assessed using a support vector machine (SVM) classifier followed by 10-fold cross-validation. In summary, we, therefore, propose the following contributions to four pairs of clinical groups (i.e., AD/HC, pMCI/sMCI, pMCI/HC, and sMCI/AD patients) based on whole-brain voxel-wise MRI features obtained at baseline:  
1.