Deep Learning Based Model for Alzheimer's Disease Detection Using Brain MRI Images

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that causes problems with memory, thinking, and behavior. And with time, symptoms become severe enough to interfere with daily activities. Although there is no cure for the disease, a proper management strategy starting at an early stage can help improve quality of life and potentially slow the disease progression. In clinical research, machine learning techniques are frequently being used in different ways to help detect disease conditions and progressions. Magnetic resonance imaging (MRI) is one of the best available tools that is used to diagnose Alzheimer's disease. However, detecting very small changes in AD brain during the early stage of the disease is challenging. In this study, we developed deep learning-based models for Alzheimer's detection using the 6219 MRI images dataset. The dataset consists of images of different degrees of demented and non-demented brains. Four deep learning models that are utilized in this study are Convolutional Neural Network (CNN), ResNet101, DenseNet121, and Visual Geometry Group16 (VGG16). From the analysis, we found that CNN outperformed other models and achieved an accuracy of 97.60%, recall of 97%, and AUC of 99.26%, with a nominal loss of 0.091.