Classification of Alzheimer’s Disease Patients Using Texture Analysis and Machine Learning

Applied System Innovation

Ketan Kotecha¹  Shilpa Gite¹  Nishad Vyas²  Saanil Khanna²  Sumit Salunkhe²  Chinmay Katpatal²  Mrinal Bachute²  Keta Modi² 

[1] Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India;Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, India;

关键词: Alzheimer’s disease;    texture analysis;    machine learning;    GLCM features;    Magnetic Resonance Imaging;   

DOI  :  10.3390/asi4030049

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown