Abstract:

The aim of this paper is to provide an overview of how machine learning can be applied to detect Alzheimer's disease. Specifically, deep learning techniques such as CNN, Mobile Net, and VGG16 are used to achieve maximum efficiency in analyzing MRI scans provided by the user. The paper also addresses the challenges and limitations of using machine learning for Alzheimer's disease detection, such as the lack of standardized datasets and the need for large datasets, as well as the importance of selecting appropriate machine learning models. Alzheimer's disease is a chronic neurodegenerative disorder that primarily affects memory and behavior, and this project offers a promising tool for detecting the disease by identifying patterns in various datasets and determining the stage of the disease. The final output is classified into four stages, namely mild dementia, no dementia, semi-mild dementia, and dementia, which helps users determine appropriate medication based on the output obtained.

Description:

Datasets, MobileNet, VGG16, Dementia, Alzheimer disease detection, CNN, Machine Learning