Alzheimer's Disorder Identification from MRI Images with EfficientNetB0

Alzheimer's disease has been defined as a progressive loss of mental function and is a neurodegenerative condition. For prompt management and better patient outcomes, it is essential to make an early and correct analysis of Alzheimer's illness. Models based on deep learning have recently demonstrated encouraging outcomes in medical image analysis applications, such as Alzheimer's disease sorting. Modern deep learning architecture EfficientNet has shown good precision on a number of computer vision applications. Proposed Research has created models for categorizing Alzheimer's ailment with neuroimaging data by utilizing EfficientNet capabilities. The model's higher performance is a result of its capacity to take advantage of the vast spatial and temporal data recorded in neuroimaging scans. This paper offers a unique method for precise and effective Alzheimer's disease classification using brain imaging data by utilizing the EfficientNet architecture. The results demonstrate the potential of deep learning models to enhance early detection and medical treatment plans for Alzheimer's disease patients.