Improving Alzheimer’s Disease Detection Based Deep Learning and Magnetic Resonance Imaging

Alzheimer's disease (AD) is a chronic neurodegenerative disease that severely affects cognitive functions, which results in a serious memory impairment accompanied by personality changes. An active examination, which is sensitive and effective on time, is a must. Cerebrospinal fluid (CSF), Magnetic Resonance Imaging (MRI), and positron emission tomography (PET) are conventional methods for diagnosing the disease that are also the most invasive, expensive, and require special knowledge. Artificial intelligence (AI) and deep learning (DL) represent one of the key areas in which these advancements have been realized. Neuroimaging is a rapidly evolving field that has played a pivotal role in the earlier diagnosis of AD. As the name implies, the objective of the research is to come up with an advanced learning model based on ResNet50 for the diagnosis of AD. For this reason, advanced preprocessing techniques as well as a data augmentation approach have been employed to address this problem. The proposed ResNet50 model achieved 99.2% test accuracy, 99% precision, 99% recall, and 99% F1-score, demonstrating its superior performance compared with the other evaluated models. The results of the experimental evaluations show the fact that the classification has a high level of accuracy which in turn confirms the reliability of the AI-perceived models in the early-stage identification of AD. Nevertheless, issues like data access, computational costs, and clinical embedding need to be dealt with in order to promote ubiquitous use. The outcomes stress the possibility of AI-generated tools to usher in a new era of Alzheimer's diagnostics.