A new model for early diagnosis of alzheimer's disease based on BAT-SVM classifier

Magnetic Resonance Images (MRI) of the Brain is a significant tool to diagnosis Alzheimer's disease due its ability measure regional changes in brain that reflect progression detect early stages disease. In this paper, we propose new model adopts Bat for parameter optimization problem Support vector machine (SVM) diagnose Alzheimer’s via MRI biomedical image. The proposed uses image classification three classes; normal controls (NC), mild cognitive impairment (MCI) and (AD). based on segmentation most involved areas hippocampus, features images are extracted build feature brain, then extracting neuroimaging reduce high dimensional space lower subspace, submitted learning technique. Moreover, applied different datasets validate efficiency which show Bat-SVM can yield promising acceptable level accuracy reached 95.36 % using maximum number bats equal 50 generation 10.