Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Reddy, Yashika P; Senthil Vadivu, M.; Jeevanand, E.S. Title A name given to the resource Alzheimer's Disease Detection using Deep Feature Extraction and Explainable Machine Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2026;pp.135-140 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSADL67539.2026.11451882" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSADL67539.2026.11451882</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036599758?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036599758?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Alzheimer's disease; Deep Learning; Explainable AI; MRI; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156883-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online