Recent papers have shown that age of the brain and brain related changes can be used in the prediction of individual age by using trained regression methods on MRI brain images. These predictions will be useful in estimating the biological brain age, to find disease and the genetical components related to brain aging. If the brain age prediction is accurate then this value or measurement can be used to find individuals brain age and the abnormalities in brain. In this Paper, we develop a different methods to predict Brain age , the main aim is to predict the brain age with accuracy. The primary method we used is Convolutional Neural Networks (CNN) and Deep Learning Framework to predict the brain age. The Convolutional Neural Networks method is based on the residual architecture and uses the deep learning covariates framework. We also used machine learning concepts for regression. Keywords: Brain-Age prediction, Brain age, Prediction of accuracy.
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL
₹10000 (INR)
NON IEEE -2021