Author : Dr. Sundharraj M 1 , Makila S 2 , Sakthi Keerthana M 3 , Vasundhara B 4 ,

Alzheimer's disease is a neurological brain disorder that progresses and is incurable. Early diagnosis of Alzheimer's disease can aid in effective care and stop brain tissue destruction. Researchers have used a number of analytical and machine learning models to diagnose Alzheimer's disease. Clinical research routinely uses magnetic resonance imaging (MRI) analysis to identify Alzheimer's disease. Because Alzheimer's disease MRI data and typical MRI data of older persons are comparable, diagnosing Alzheimer's disease can be challenging. In many disciplines, including medical image processing, cutting-edge deep learning approaches have recently effectively proven performance at the level of a human. By analyzing brain MRI data, we suggest a deep convolutional neural network for diagnosing Alzheimer's disease. Our model performs better for early-stage diagnosis and can recognise different phases of Alzheimer's disease than most existing techniques, which mostly do binary classification.

Full Text Attachment