Alzheimer's Disease Identification and Categorization Through Deep and Machine Learning

Abstract

Alzheimer's disease can be difficult to detect early, which restricts timely diagnosis and treatment options. In this work, we present a practical method for identifying and staging Alzheimer's disease that combines regularly recorded clinical symptoms with brain imaging. By employing explainable artificial intelligence techniques to identify relevant brain regions in addition to significant early warning indicators, the method improves accuracy and offers useful interpretation. In clinical neurology, early and precise identification of Alzheimer's disease (AD), particularly at the Mild Cognitive Impairment (MCI) stage, continues to be a major issue. Although deep learning models have shown remarkable success in diagnosing AD using clinical data and neuroimaging, their opaque nature undermines trust and acceptance in medical settings. This work offers a dual-modal approach that uses explainable AI (XAI) to augment machine learning (ML) and deep learning (DL) models to integrate symptom-based clinical data with magnetic resonance imaging (MRI). Methods: Using clinical and demographic data, four machine learning classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were trained. Five DL models were used to MRI data for stage-wise classification: CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2. Grad-CAM and SHAP visualizations were used to incorporate interpretability. The results of this study may help with clinical decision-making and provide a flexible foundation for future research to create Alzheimer's detection and staging methods that are more accurate, understandable, and accessible.