# Classification of Alzheimer's Disease Patients Using Texture Analysis and Machine Learning
###### Abstract
Angular Second Moment (ASM), Inverse Difference Moment, Entropy and Correlation were the second-order features calculated using the GLCM. High discrimination accuracy was achieved by offering these features for image approximation [5]. [PERSON], [PERSON] et al., considered the bilateral hippocampus and medial temporal lobe (MTL) for volumes and texture features in AD subject's MRI. Texture features were extracted using GLCM on 66 subjects; 29 of these were AD subjects. Texture features and corresponding volumes were analyzed for correlation between them. Volumes and texture features between the AD and CN group in both the hippocampus and MTL were found to be significantly different. The texture features matching to volumes of both positions were correlated. The CN group was further classified into the non-demented (EC) and young control (YC) group. Texture analysis and volumetry may aid in AD diagnosis, indicating no significant difference in CN groups. It implies that atrophy is observed to be distinct in AD and be an age and AD-associated change [6]. [PERSON], [PERSON] et al., describes how to use the GLCM to extract texture features for image motion approximation. The Xilinx FPGA was used to compute the four features: ASM, Correlation, Entropy, and Inverse Difference Moment. The findings confirm that the texture features have excellent discriminating precision, take minimal calculation time, and can thus be employed effectively for real-time pattern recognition applications [7]. [PERSON], [PERSON] et al. investigated the efficacy of classifying CN and AD patients relying on brain texture features. As for shape features, a P-type Fourier descriptor was used, and the lateral ventricle except for the septum lucidum was studied. They had used SVM to conduct classification utilizing a collection of multiple characteristics as features. The classifier accuracy was 87.5%, which was higher than the accuracy reached using volume ratio to intracranial volume (81.5%), which is frequently recognized for traditional evaluation of degenerative characteristics. As per recent research, shape information may be more relevant in diagnosis than traditional volume ratio [1]. [PERSON] et al. examined AD in T1-weighted MRI combining Texture Features and the k-NN Classifier. Texture features were obtained via GLCM. With the sole purpose of evaluating the suggested approach, tests were undertaken on the MRI obtained from the OASIS dataset. The presented method has a typical testing accuracy of 74.73%, suggesting that it can differentiate AD and CN effectively [3]. [PERSON] et al. intended to better understand the disease's development by evaluating neuropsychological test results based purely on MRI data. To accomplish this, they used a deep convolutional auto encoder to do a self-supervised segmentation of the MRI data. The dispersion of features in the z-layer was then employed in a neural regression to a complex linear relationship. Then to see if these imaging-derived indicators are associated with various neuropsychological tests. \(R^{2}\) rates of more than 0.3 were achieved in predicting neuropsychological analysis results, with correlations greater than 0.5 in the case of variables significantly associated with neurodegeneration and cognitive states, such as the MMSE or the ADASS11 scores [8; 9; 10].