## 3 Experiments
### Discussion
A significant improvement has been noticed in terms of sensitivity as compared to AD/MCI and MCI/HC experiments for all features, i.e., RM, PCET, ZM, and PCT. Again, RM features show the lowest performance among all the features. PCT-based features give the highest performance for all indices followed by ZM, PCET, and RM. However, no statistical significance is shown between the PCT, PCET, and ZM feature sets. For all cases, RM textural features show poor performance. This is because the orthogonal moments (PHTs) have proven superior feature representation capability and low information redundancy compared to non-orthogonal moments (RM). We also plotted the regions which are most responsible for distinguishing between normal and disease. Figure 3 shows the most affected regions during dementia through the coronal, sagittal, and axial view. We observed that the temporal middle gyrus right, frontal middle gyrus right, precentral gyrus lefts, left middle occipital gyrus are the most affected regions during the AD progression. Figure 2: Receiver operating characteristics (ROC) curve for (**a**) Alzheimer’s disease (AD) vs. mild cognitive impairments (MCI), (**b**) AD vs. healthy controls (HC), and (**c**) MCI vs. HC. Finally, a brief comparison between the recently developed methods most related to our approach has been presented in Table 2. However, it is quite difficult to compare these methods in a precise manner since different researchers have employed different feature sets and classification methods. According to Table 2, the proposed PHT descriptors represent fair AUC, which is comparable with the other reported systems [46]. The proposed method shows lower sensitivity compared to [PERSON] et al. [46] (100% sensitivity). However, it is superior to [PERSON] et al. (2015) in terms of specificity, accuracy, and AUC for HC vs. MCI cases. Moreover, for MCI vs. AD cases, the proposed method shows the best performance for all indices (sensitivity, specificity, accuracy, and AUC). The feature extraction and data preparation techniques are very similar to [27] except for validation technique, segmentation, and classification scheme. However, the proposed method attains lower sensitivity, accuracy, and specificity for AD/MCI and MCI/HC compared to the results reported in [27]. For MCI/AD, the best results are reported by [PERSON] et al. [45]. Our result is concordant with [PERSON] et al. [47] for statistical feature analysis and RM. In recent years, several research efforts have been made for the classification of AD using machine learning [48; 49; 50]. In particular, most of these machine learning approaches looked at the potential of feature extraction using MRI and diffusion tensor imaging [51]. Several other efforts also have been made in multi-modal imaging, which shows that multimodal imaging can enhance the accuracy of classification. In the literature, most of the studies are concerned with gray matter features of the brain. However, progress in diffusion tensor imaging also opening a window to the researchers to explore white matter connectivity features. However, the classification accuracy of the classifier in classifying AD using white matter ranges between 72% and 82% in the literature. Gray matter shows accuracy above 85%. Some studies also show that multimodal features (MRI, FMRI) can increase accuracy above 901% [52; 53; 54].