Hybrid Feature Extraction and Classification for Alzheimer’s Disease Detection

Alzheimer’s disease (AD) is a serious neurological brain disease. It terminates brain cells, causing loss of memory, mental function and the capability to continue their daily actions. AD is incurable, but early detection can greatly improve symptoms. Machine learning can greatly develop the accurate analysis of AD. In this paper, we have implemented the two different hybrid algorithms for feature extraction and classification. Hybrid feature extraction algorithm is based on Empirical mode decomposition (EMD) and Gray-Level Co-Occurrence Matrix (GLCM), which is named as EMDGLCM. For classification purpose Support vector machine (SVM) and Convolution neural network (CNN) which is named as SVM-CNN. The proposed hybrid algorithm feature extraction and classification Improves the proposed system performance the proposed system has analysis with the help of OASIS dataset. The proposed results and comparative results shows that the proposed system provides the better results.