Efficient Alzheimer's disease detection using deep learning technique

The human brain serves as the primary control centre for the humanoid system. Computer vision plays a vital part in the field of human health, which helps to reduce the amount of human judgement that is required to produce accurate findings. Scans using computed tomography, X-rays, and magnetic resonance imaging (MRI) are the most popular imaging technologies used in MRI, and they could also the greatest trustworthy and safe. The MRI can identify even the most minute of objects. In this paper, Alzheimer's disease detection in early stage, based on MRI by using the deep learning technique U-Net and EfficientNet which is a convolutional neural network, is implemented. Diagnosing Alzheimer's disease (AD) accurately is an vital aspect in treating AD patients, eventually during the early disease stages. This is particularly true in the early disease stages of the disease, when awareness of risk enables AD patients to take up protective measures well before the occurrence of brain damage that cannot be reversed. Despite of the fact that computers have been utilised in a significant number of recent research to diagnose AD, the majority of machine detection approaches are restricted by congenital findings. Early-stage Alzheimer's disease (AD) can be identified, but early-stage AD cannot be predicted because prediction of the disease is successful only before the (AD) disease reveals itself. Deep learning, often known as DL, has recently emerged as a popular method for the initial recognition of Alzheimer's disease (AD). In this article, we will give a quick overview of some of the key research that has been done on AD, and we will investigate how DL can assist researchers in the early phases of disease diagnosis.