# Classification of Alzheimer's Disease Patients Using Texture Analysis and Machine Learning
###### Abstract  
Alzheimer's disease (AD) has been studied extensively to understand the nature of this complex disease and address the many research gaps concerning prognosis and diagnosis. Several studies based on structural and textural characteristics have already been conducted to aid in identifying AD patients. In this work, an image processing methodology was used to extract textural information and classify the patients into two groups: AD and Cognitively Normal (CN). The Gray Level Co-occurrence Matrix (GLCM) was employed since it is a strong foundation for texture classification. Various textural parameters derived from the GLCM aided in deciphering the characteristics of a Magnetic Resonance Imaging (MRI) region of interest (ROI). Several commonly used image classification algorithms were employed. MATLAB was used to successfully derive 20 features based on the GLCM of the MRI dataset. Based on the data analysis, 8 of the 20 features were determined as significant elements. Ensemble (90.2%), Decision Trees (88.5%), and Support Vector Machine (SVM) (87.2%) were the best performing classifiers. It was observed in GLCM that as the distance (d) between pixels increased, the classification accuracy decreased. The best result was observed for GLCM with \(d=1\) and direction (\(d\), \(d\), \(-d\)) with age and structural data. Alzheimer's disease; texture analysis; machine learning; GLCM features; Magnetic Resonance Imaging 202128 July 2021 4 MRI is a highly effective technology for nonaggressive in vivo imaging of the human brain. A widely proven approach is used for robust cross-sectional and longitudinal segmentation of Alzheimer's Disease Neuroimaging. Initiative Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort brain MRI. Structural Magnetic Resonance Imaging (sMRI) detects subtle anatomical changes in the brain to detect AD [3]. MRI is favoured over other modalities for detecting structural changes in the brain caused by neurodegenerative diseases and its significance in AD diagnosis and prognosis [4]. In most sMRI investigations, large-scale tissue changes have been used as biomarkers based on derived features like atrophy to acquire a better diagnosis of AD. Subtle neurodegenerative changes can be noticed and analyzed using statistical properties acquired from texture analysis on an MRI based on a biomarker. Medical imaging is obtaining images of body parts for diagnostic applications such as detecting or studying diseases. Every week, millions of imaging tasks are performed around the world. Medical imaging is quickly evolving because of advances in image processing methods such as image recognition, analysis, and enhancement. Image processing improves the proportion and quantity of recognized tissues. Machine learning is a pattern recognition technique that can be used in medical imaging. It is a vital tool that can aid in medical diagnosis. Typically, machine learning begins with the machine learning algorithm system determining the image attributes that are thought to be important in producing the desired prediction or diagnosis. The machine learning algorithm system then determines the optimum solution of these image attributes for classification. The contributions of this research work are:  
* To use texture analysis to examine the minor changes in the hippocampus caused by AD. * To extract features by implementing texture analysis technique on MRI. * To classify subjects into AD and cognitively normal (CN) groups by providing the information extracted as input for training machine learning models. An extensive literature survey was performed, which helped delimit the research domain, and focus is directed to the crucial issues to be resolved. Gray Level Co-occurrence Matrix (GLCM) has been verified to be a standard statistical process of extricating textural characteristics from illustrations. The number of grey levels in the image are equivalent to the rows and columns of a GLCM. GLCM was used to acquire textural features.