• S.Suneetha
• A.Dileep
• T.Srinu
• D.Barath
• R.Prabhas

In this study, convolutional neural networks are used to make estimates of brain age and the categorization of brain diseases. To better pinpoint age-related imaging biomarkers using structural magnetic resonance imaging, researchers have been looking at brain morphometric pattern analysis (MRI). MRI is a noninvasive method that may be more sensitive than other biomedical imaging modalities for detecting aberrant structural brain alterations. Brain MRI scans were used to classify abnormality/normality including Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Healthy Control (HC). The effectiveness of the tried-and-true Random Forest Classifier is examined, implemented, and compared to that of the cutting-edge deep learning technology known as Convolutional Neural Network (CNN). Following these steps, an estimated brain age is calculated that takes into account the different abnormality/normality types.

Convolutional neural network, Deep learning, Random Forest Classifier, Magnetic resonance imaging

"BRAIN DISEASE DETECTION & BRAIN AGE ESTIMATION USING CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.b722-b725, March-2023, Available :http://www.jetir.org/papers/JETIR2303194.pdf

"BRAIN DISEASE DETECTION & BRAIN AGE ESTIMATION USING CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppb722-b725, March-2023, Available at : http://www.jetir.org/papers/JETIR2303194.pdf