## 4 Conclusions  
In the current research, a framework of computer-aided diagnosis (CAD) system using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN) has been presented to classify dementia like Alzheimer's disease (AD), mild cognitive impairments (MCI), and healthy controls (HC). The proposed method  
Figure 3: Brain plot for regions that are most affected during the AD progression. From top to bottom are coronal, sagittal, and axial views.  
works in two phases in which the first one aims to extract the Region-of-Interest (ROI) area using a combination of image processing techniques. The second phase uses feature extraction using PHT and classification with the SaDE-WNN method. The features are firstly extracted by utilizing the properties of the PHT from a structural MRI database followed by a series of image pre-processing steps. The features are then selected by evaluating the in-class and among-class variance of the training database. Finally, the extracted features are classified accordingly using the proposed SaDE-WNN method.  
The proposed method has been validated on the Alzheimer's disease Neuroimaging Initiative (ADNI) database. The AUC, the statistical consequence of differences between ROC index and parameters were calculated and compared by employing the z-test. The new method achieved better results than the relevant works; specifically, the overall classification results between AD and MCI, MCI and HC, and for pairs of the AD vs. HC MCI. The classification results were statistically significant between orthogonal moments (RM) and non-orthogonal moments (PHTs). It is finally concluded that the proposed method gives better results specifically in terms of sensitivity which was the main concern of the current research.  
Future works in this research field will investigate the integration of Deep Learning to train the SaDE-WNN and enhance the computational cost of the proposed method in this study. In this paper, we used a single modality of images only. A combination of different modalities can increase the effectiveness of the proposed method such as T1, T2. We could also test the prosed method using GM and white matter or using a combination of MRI and fMRI.  
Conceptualization, S.U. and S.P.S.; methodology, S.U. and S.P.S.; software, S.P.S.; validation, S.U. and S.P.S.; formal analysis, S.U., A.M. and F.A.; investigation, S.P.S.; resources, S.U., A.M., S.K. and F.A; data curation, A.M., S.K. and F.A; writing--original draft preparation, S.U. and S.P.S.; writing--review and editing, S.P.S.; visualization, S.P.S.; supervision, A.M., S.K. and F.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia. The project number is PNU-DRI-RI-20-019.  
Not applicable.  
Data used in this study is available at [[http://adni.loni.usc.edu](http://adni.loni.usc.edu)]([http://adni.loni.usc.edu](http://adni.loni.usc.edu)).  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research through project number PNU-DRI-RI-20-019.  
The authors declare no conflict of interest.