Unraveling Dementia Severity: A Deep Learning Approach for Brain MR Image-Based Prediction and Misclassification Analysis

Alzheimer’s disease is the most prevalent cause of dementia, and its early diagnosis is crucial to prevent the progression to severe stages where cognitive abilities are severely impaired. This research paper presents an innovative approach to predict the severity of dementia through classification and grading. The research introduces an innovative adaptation of the DEMNET model, referred to as the DEMENtia network model. The research implements a novel methodology leveraging Convolutional Neural Networks (CNNs) to identify significant patterns within unorganized web-based data collections. The investigation employs a dataset com- prising four categories, obtained from the Kaggle platform. The developed model demonstrates exceptional performance, achieving 99.9% accuracy during training, 97.4% accuracy in testing, and an overall precision of 0.975. The DEMENtia network model suc- cessfully categorizes individuals into four groups: those without dementia, and those with moderate, mild, or very mild dementia. The model achieves a remarkable accuracy of 99.20% in classifying the Moderate demented class, a significant advantage over existing approaches. To understand this behavior, conducted an in-depth analysis by visualizing the pixel intensity distribution over the space. The proposed model validity has been confirmed through validation by a team of neurologists, ensuring its potential for real-world clinical applications. By accurately predicting dementia severity, the proposed model can aid in early diagnosis and treatment planning, contributing to improved patient care and management.