Application of Artificial Intelligence and Machine Learning Techniques in Classifying Extent of Dementia Across Alzheimer's Image Data

Abstract

Alzheimer's disease (AD) is one of the most common forms of dementia and the sixth-leading cause of death in older adults. The presented study has illustrated the applications of deep learning (DL) and associated methods, which could have a broader impact on identifying dementia stages and may guide therapy in the future for multiclass image detection. The studied datasets contain around 6,400 magnetic resonance imaging (MRI) images, each segregated into the severity of Alzheimer's classes: mild dementia, very mild dementia, non-dementia, moderate dementia. These four image specifications were used to classify the dementia stages in each patient applying the convolutional neural network (CNN) algorithm. Employing the CNN-based in silico model, the authors successfully classified and predicted the different AD stages and got around 97.19% accuracy. Again, machine learning (ML) techniques like extreme gradient boosting (XGB), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN) offered accuracy of 96.62%, 96.56%, 94.62, and 89.88%, respectively.