Publication

Abstract : There is no recognised treatment for the neurological illness known as Alzheimer's disease (AD). Early diagnosis and suitable treatment are advantageous. Deep Learning algorithms have proven successful in several areas, including the diagnosis of AD. This study achieves good accuracy (training: 86.34%, validation: 86.45 % ) for AD identification using MRI data and a convolutional neural network (CNN). The accuracy, quick processing, and population-level generalizability of the CNN architecture demonstrate its clinical use in categorising Alzheimer's disease. The system created in this study makes use of MRI scan images that were trained on the Kaggle dataset, highlighting how crucial consistent data is for model analysis and evaluation.