Alzheimer's Disease Detection using Deep Feature Extraction and Explainable Machine Learning
subject: Alzheimer's disease; Deep Learning; Explainable AI; MRI; XGBoost
description: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by gradual cognitive decline, posing significant diagnostic challenges that necessitate automated detection systems to aid clinical decision-making. This study presents an explainable machine learning framework for binary dementia classification using deep feature extraction from magnetic resonance imaging. A pretrained ResNet50 convolutional neural network was employed to extract 2048-dimensional feature vectors from 86,437 MRI slices derived from the OASIS1 dataset, encompassing 347 subjects. The dataset was imbalanced, containing 67,222 Non-demented and 19,215 demented slices (combining very mild, mild, and moderate dementia). The aggregated features at the Subject-level were used to train three machine learning classifiers: Logistic Regression, Random Forest, and XGBoost. The XGBoost model achieved the highest accuracy of 77.14, with a precision of 0.84 and a recall of 0.87 for Nondemented cases, demonstrating strong discriminative capability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations highlighted the hippocampus and temporal lobes as key regions influencing predictions, aligning with established Alzheimer's pathology. The study demonstrates the potential of combining deep feature extraction with interpretable machine learning for automated dementia screening. 2026 IEEE.
creator: Reddy, Yashika P; Senthil Vadivu, M.; Jeevanand, E.S.
source: Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2026;pp.135-140
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833156883-2;
language: English
type: Conference paper
coverage: Reddy Y.P., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Senthil Vadivu M., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India; Jeevanand E.S., Christ (Deemed to be University), Department of Statistics and Data Science, Karnataka, Bengaluru, 560029, India