A Detailed study on Image Segmentation with Different Optimization Algorithms for Dementia Disease

The most effective and often used method for diagnosing Alzheimer disease using magnetic resonance images (MRIs) is deep learning (DL), which attracts the attention of most researchers and spurs more study in this area. According to the experts, deep learning technology is essential for interpreting medical images from a variety of angles. The most significant aspect of handling the numerous unconstructed datasets and having to extract the necessary and crucial characteristics from the datasets is how Convolution Neural Networks are mostly utilised to processing the medical imaging datasets. The fundamental objective of researchers investigating brain illnesses, particularly Alzheimer's disease a type of dementia, is to identify and diagnose the damaged area as soon as possible. This study provides a complete assessment of several optimisation techniques for the segmentation of the specified region for diagnosing the memory related diseases. The methods that enable a Support Vector Machine (SVM) for segmenting the essential region for Alzheimer's categorization, namely the Hippocampus, Grey Matter, White Matter, CSF Fluid, Ventricles, and Amygdala.