# Classification of Alzheimer's Disease Patients Using Texture Analysis and Machine Learning
## 4 Conclusions  
AD is one of the world's leading causes of death from neurological disorders. The number of people with Alzheimer's is steadily rising over the world. The neurologist's manual diagnosis approach for AD is time-consuming and may not always yield correct results. One of the most significant elements in using brain images to classify AD is feature extraction. It is revealed that all 20 features retrieved from the GLCM of the participants' MRI are not required. It is observed that the crucial eight features contribute the most to the classification of the individuals using the F-type statistics test. According to the results, the classification accuracy declines as the distance between neighbouring pixels increases. The comparisons demonstrate that using structural data, textural data, and age as input combined improves classification accuracy. The top-performing algorithms were Ensemble and Decision Trees, with a classification accuracy of 90.2% and 88.5%, respectively, and SVM, with a classification accuracy of 87.2%. The subject classification was on par with research articles that have used feature extraction using various image processing techniques [39].  
Conceptualization, S.S. and M.B.; methodology, S.S. and N.V.; software, S.S.; validation, S.S. and M.B.; formal analysis, S.S. and N.V.; investigation, S.S. and N.V.; resources, S.S.; data curation, S.K., K.M. and C.K.; writing--original draft preparation, S.S., N.V., S.K., K.M. and C.K.; writing--review and editing, S.S., M.B., N.V., S.K., K.M. and C.K.; visualization, S.S. and N.V.; supervision, K.K. and M.B.; project administration, S.S. Corrections and checking, M.B. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were waived for this study, due to freely publicly available data used for experimentations.  
Patient consent was waived due to freely publicly available data used for experimentations.  
The data presented in this study are available on request from the corresponding authors.  
The authors would like to thank both SIU and SIT for their support.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Structure** & \begin{tabular}{c} **Texture Analysis** \\ **Method (Features)** \\ \end{tabular} & \begin{tabular}{c} **Machine Learning** \\ **Technique** \\ \end{tabular} & **Accuracy (\%)** \\ \hline [PERSON], [PERSON] et al. [6] & Brain & GLCM, Gabor filter & SVM & For AD-CN data: 85.71 \\ \hline [PERSON], [PERSON] et al. [33] & \begin{tabular}{c} Gray Matter, White \\ Matter, Cerebral Spinal \\ Fluid and Background \\ \end{tabular} & \begin{tabular}{c} Coefficient of \\ Probability Changes \\ \end{tabular} & SVM & \begin{tabular}{c} Maximum: 95 \\ Average: 70 \\ \end{tabular} \\ \hline [PERSON] et al. [34] & Brain & GLCM & k-NN & 74.73 \\ \hline [PERSON] et al. [35] & Brain & 3D GLCM & - & \
\begin{tabular}{c} Maximum: 90.5 \\ Average: 76 \\ \end{tabular} \\ \hline [PERSON] et al. [36] & Brain & 3D GLCM & Ensemble & 74.19 \\ \hline [PERSON] et al. [37] & Hippocampus & 3D GLCM & SVM & 86.61 \\ \hline [PERSON] et al. [38] & Hippocampus & Hippocampal Volume &
\begin{tabular}{c} Diagonal Quadratic \\ Discriminant Analysis \\ (Naïve [PERSON]) \\ \end{tabular} & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy based on texture analysis and machine learning techniques.