Deep Learning Algorithms for Brain Disease Classification and Magnetic Resonance Imaging Age Estimation

Abstract

It is possible to utilize deep neural networks to accurately estimate the chronological age of healthy people using neuroimaging data; this information might then be used as a biomarker to detect diseases associated with aging. A deep learning cascade network called Convolutional Neural Network (CNN) and a machine learning algorithm called Support Vector Machine (SVM) are therefore used in the suggested approach. Using these algorithms, we have been able to train MRI images of the brain into three distinct categories: normal, in which no illness has had an effect, Alzheimer's disease (AD), and mild cognitive impairment (MCI). and age them based on their classifications. The main ways convolutional neural networks (CNNs) and support vector machines (SVMs) are trained on MRI image datasets are for classification and age estimation. Machine learning, convolutional neural networks, deep learning, SVM, magnetic resonance imaging, and age estimate are all related terms. Pydala Bhasha, Indian Institute of Technology (IIT) at Mohan Babu University in Tirupati Contact information: basha.chanti@gmail.com Image Example in Figure 1 No medication can prevent or reverse the progression of an illness, even if some may rapidly lessen adverse symptoms. A heavy burden is often placed on guardians as those affected become more reliant on others for assistance. Factors that contribute to pressure include social, psychological, physiological, and economic factors. Routines might be useful for planning day-to-day tasks and could even lead to better outcomes. Just like the I in figure 1.