Deep Learning Models for Early Detection of Alzheimer’s Disease Using Neuroimaging Data

Abstract

Early identification is essential for successful intervention in Alzheimer's disease, a progressive neurodegenerative disease that is a major contributor to cognitive loss in older persons. Alzheimer's disease is difficult to detect in its early stages using conventional diagnostic techniques like neuroimaging and cognitive tests. This study investigates the use of deep learning models specifically, Convolutional Neural Networks, or CNNs to neuroimaging data to diagnose Alzheimer's disease early and the prognostic ability of Alzheimer-signature MRI biomarkers in detecting the change in cognitively normal persons into those with Alzheimer's disease (AD) dementia. Based on secondary data taken from the literature, this study assesses the performance of many deep learning architectures, such as Dense Net models, Graph Convolutional Networks (GCNs), and 3D CNNs as well as biomarkers. According to our research, CNN-based models hold great potential for precise Alzheimer's disease identification, particularly when they use three-dimensional imaging data. CNNs are the most commonly used architecture, according to a comparative study of 22 reviewed research; other models, such as GCNs and fine-tuned VGG19, exhibit noteworthy performance. The clinical applicability of such deep learning techniques and their capacity to improve patient outcomes and diagnostic precision in Alzheimer's care are also covered in this research. The study ends with suggestions for additional research, with an emphasis on addressing dataset variability limits and optimizing the model.