Alzheimer's disease Diagnosis from MRI using Siamese Convolutional Neural Network

Abstract

AD is a neurological illness. It ranks as the sixth most common reason for both morbidity and mortality. Alzheimer's disease can progress through three stages: mild, moderate, and severe. A timely diagnosis can assist in the provision of necessary therapy, so preventing additional harm to brain tissue. Recent research has utilised technology in an attempt to diagnose Alzheimer's disease; nevertheless, the majority of machine detection technologies are inborn. The early stages of Alzheimer's disease can be diagnosed, but it is not possible to anticipate the progression of the disease. Prediction is only possible before dementia sets in. Deep Learning (DL) has the potential to detect Alzheimer's disease in its early stages. In this article, we use two different kinds of data to predict disease categories: csv data that includes cognitive task parameters like SES, MMSE, CDR, eTIV, nWBV, ASF, delay, heredity, MOCA, SAGE, CDT; and basic patient information like gender, age, dominant hand, Education, drowsiness, and visits. The csv data includes cognitive task parameters like SES, MMSE, CDR, eTIV Calculations are done to determine the F1 score, precision, recall, and accuracy of each technique.