Alzheimer's Disease Shape Detection Model in Brain Magnetic Resonance Images Via Whale Optimization with Kernel Support Vector Machine

Structural brain imaging is a significant part of detecting the brain's changes related to Alzheimer's disease (AD). It harms the brain cells, and the earlier identification becomes the need to provide proper medication. Magnetic resonance imaging is widely applied to identify and analyze the disease's growth, influencing the medication process. This view keeps designing the computer-aided diagnosis tool for distinguishing images with AD. This study develops an efficient automatic diagnosis model for brain MRI affected by AD. The presented model initially employs a discrete wavelet transform for feature extraction and then used principal component analysis for feature reduction. The kernel support vector machine with a whale optimization algorithm is applied for parameter optimization. An extensive simulation is executed to confirm the superiority level of the presented model. The experimental results say that the WOA-KSVM model is an appropriate diagnostic tool for screening AD.