Deep Transfer Learning For Improving Alzheimer Disease Diagnosis

Abstract

Alzheimer disease (AD) is a neurological disorder which shrinks the brain and causes dementia. In the past, this disease was more prevalent in American countries. However, it is now common in other countries as well. When compared to youth, older people are more affected by this disease. The number of people affected by this disease is gradually increasing each year, and according to one study, this number may reach around 15 million in the near future. People who are affected will experience symptoms such as memory loss and confusion. Early detection of Alzheimer disease is essential for providing appropriate treatments. Neuroimaging based Machine Learning methods are commonly utilized for the detection and diagnosis of Alzheimer’s, but they are time-consuming. The time consumption can be reduced, and the detection accuracy can be increased further with the help of Deep Learning and Transfer Learning algorithms. This proposed work compares 4 different Transfer Learning Models. VGG-16 has the highest accuracy of 97.2 percent out of the four models tested.