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 toolto diagnosis Alzheimer's disease due to its ability to measure regionalchanges in the brain that reflect disease progression to detect earlystages of the disease. In this paper, we propose a new model thatadopts Bat for parameter optimization problem of Support vectormachine (SVM) to diagnose Alzheimer’s disease via MRI biomedicalimage. The proposed model uses MRI for biomedical imageclassification to diagnose three classes; normal controls (NC), mildcognitive impairment (MCI) and Alzheimer’s disease (AD). Theproposed model based on segmentation for the most involved areas inthe disease hippocampus, the features of MRI brain images areextracted to build feature vector of the brain, then extracting the mostsignificant features in neuroimaging to reduce the high dimensionalspace of MRI images to lower dimensional subspace, and submittedto machine learning classification technique. Moreover, the model isapplied on different datasets to validate the efficiency which showthat the new Bat-SVM model can yield promising acceptable level ofaccuracy reached to 95.36 % using maximum number of bats equal to50 and number of generation equal to 10.