Overview

Original Research

DETERMINING EFFECTIVE LEVEL OF DEMENTIA DISEASE USING MRI IMAGES

KAUSHAL KISHOR BHATT 1, and PARVEEN SEHGAL 2.

Vol 18, No 09 ( 2023 )   |  DOI: 10.5281/zenodo.8375092   |   Author Affiliation: Research Scholar, Department of Computer Science, Om Sterling Global University, Hisar, Haryana 1; Professor, Research Supervisor, Department of Computer Science, Om Sterling Global University, Hisar, Haryana 2.   |   Licensing: CC 4.0   |   Pg no: 477-487   |   Published on: 19-09-2023

Abstract

Abstract

The prevalence of dementia is growing as the world's population ages, making it a major public health issue. The key to successful management and treatment of dementia is an early and precise diagnosis. In this work, we will investigate the Dementia detection model DenseNet-169 in depth. The DenseNet-169 model has been used to classify almost 7,000 magnetic resonance imaging (MRI) scans of the brain. Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia are all categorized using this Convolution Neural Network (CNN) model. The use of deep learning and image processing presents intriguing new directions for the diagnosis and treatment of dementia, with the ultimate goal of enhancing the quality of life for those with the disease.

Keywords

Dementia, Mild, Severe, Image Processing, Deep Learning