Enhancement of Alzheimer’s Disease Stage Classification Using Deep Learning BasedPre-trained Models

Alzheimer’s disease (AD) is a neurodegenerative disorder that is neurodegenerative followed by a progressive decline in cognitive ability and memory loss. Early detection and prediction of AD can significantly impact treatment strategies and improve patient outcomes. In the latest years, Machine Learning Techniques (MLT), especially DLT-Deep Learning Techniques are providing promising outcomes in various medical applications. This work presents a study that targets to predict Alzheimer’s disease using pretrained models VGG16, VGG19, inception_v3 and DenseNet model. An extensive dataset of scanned brain images, such as magnetic resonance imaging (MRI) scans, from people with and without AD is being collected for the study. The columns of the dataset that are pertinent to AD are enhanced and normalized using preprocessing procedures. The input dataset is then segregated into validation set, training set and testing sets to develop and evaluate the deep learning models.