Brain Disease Classification Along with Age Estimation from MRI

Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. Hence, in this proposed method we are using the cascade network of the deep learning which is a Convolutional Neural Network (CNN) and also one of the machine learning algorithm named Support Vector Machine (SVM). These algorithms are been used to train the brain  MRI images which are considered in the three classes as the Normal which is not affected with any disease and the other classes which were affected with Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI). From which we can also find out the ages from the classified images. CNN and SVM are mainly used for the training of the MRI image dataset, upon where the classification will be performed along with the age estimation.

Keywords: Age estimation, Alzheimer’s disease (AD) and Mild Cognitive Impairment, CNN, deep learning, SVM, machine learning.