Memory Loss and Alzheimer's Disease Progression Convolutional Neural Networks with Dropout Layers for Optimal Filtered Features in MRI Images of the Hippocampus for Slice Selection Based on Landmarks

The public health threat of Alzheimer's disease (AD) is now widely accepted. When using machine learning techniques and MRI scanning to detect Alzheimer's disease, the hippocampi are readily accessible and one among the most afflicted brain regions. AD classification by machine learning algorithms using complete MRI slices was unsatisfactory. This article describes how to choose MRI slices using hippocampus landmarks. This research aims to find the best accurate AD categorization MRI pictures. Next, utilizing Resnet50 or LeNet using various classifiers with the open-source and free ADNI dataset, the three views and categories were valued. The models used 4,500 Neuroimaging slices from three perspectives and categories for training. We found that AD classification was better with MRI scan segments than whole slices. The coronal view showed our method's machine learning accuracy enhancement most clearly. This strategy greatly enhanced machine learning accuracy. The findings from a rotational perspective matched what clinicians use to identify AD. Additionally, LeNet models may classify AD effectively.