OPTIMAL FEATURES SELECTION FOR ALZHEIMER’S DISEASE PREDICTION
Keywords:
Alzheimer’s disease, GLCM, DCNN, Feature selection, GOA AlgorithmAbstract
Major Challenge for healthcare in the 21st century is dementia. It is the most common form of Alzheimer’s disease (AD). Early detection of AD is necessary for preventing the progression of the symptoms. The goal of this study is to initiate a new predictive model for AD employing MRI images. The developed model involves Feature Extraction, Optimal Feature selection, and Classification. Initially, the Gray-Level Co-Occurrence Matrix (GLCM) and Haralick features are extracted from MRI images. Especially, this work carries out optimal feature selection using a Grasshopper Optimization Algorithm (GOA). Then, the optimally chosen features are classified via Deep Convolution Neural Network (DCNN). Finally, the eminence of the adopted scheme is validated in terms of various measures.
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