Computer Aided Diagnosis System Feature Extraction of Alzheimer Disease Using MFCC

Abstract

Mel-Scale Frequency Cepstral Coefficients (MFCC) is very efficient technique for feature extraction. This paper proposes a Computer Aided Diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer Disease (AD) using MFCC technique for the 3-D MRI images. Classification is performed using Linear Support Vector Machine (SVM). Experimental results represent that the proposed CAD system using MFCC for AD recognition give excellent accuracy with small number of significant extracted features which reduces the memory size and simplify the hardware implementation of the CAD system. The proposed approach have better performance and stability.

Keywords::

• Diagnosis
• Alzheimer's Disease
• Computer Aided Diagnosis
• Feature Extraction
• Mel-Scale Frequency Cepstral Coefficients
• Feature Reduction
• Support Vector Machine

Additional information

Notes on contributors

Mohamed M. Dessouky

Mohamed M. Dessouky was born in Egypt, 1984. Graduated from Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufiya University, Egypt in 2006 with general grade excellent with honor degree. Demonstrator in 2007, Assistant Lecturer in 2011. He is currently a PhD student. His major field of study is image processing and artificial intelligence. Mohamed has more than six years of teaching experience as an assistant lecturer, and as a Teaching Assistant for a variety of undergraduate courses in different Computer Science and Engineering fields. Mohamed is a CISCO Certified Instructor and has received an award from CISCO as a best instructor for more than 5 years.