Classifying Alzheimer Disease using VGG19

A disease called as Alzheimer, always put health agencies in alarming situation and thus necessitating targeted interventions. This study employs the VGG19 convolutional neural network (CNN) in conjunction with the ADNI dataset to enhance the precision of Alzheimer’s detection. The ADNI dataset, a comprehensive repository of neuroimaging data spanning various cognitive states, serves as the foundation for robust Alzheimer’s detection. Leveraging the VGG19 CNN architecture renowned for its image classification capabilities, we analyze three-dimensional MRI scans to discern subtle patterns indicative of Alzheimer’s disease. Through fine-tuning and transfer learning, our research adapts VGG19 to accurately detect Alzheimer’s disease and its several subclasses. Further this study tries to illuminate rapid progression of the concerned disease. Despite challenges such as Alzheimer’s complexity, high-dimensional MRI data, and ethical considerations, our findings represent a significant advancement in Alzheimer’s disease identification. By integrating technology and medicine, this research not only enhances diagnostic accuracy but also contributes to the broader understanding of Alzheimer’s disease.