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 Singh, Aishwarya; Raju, G.; Bijeesh, T.V.; Bejoy, B.J. Title A name given to the resource Leveraging Deep Learning for Early Detection of Alzheimer's Disease from MRI Scans Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;1;pp.3157-3163 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105034188011?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034188011?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 Singh A., CHRIST University, Department of CSE, Bangalore, India; Raju G., CHRIST University, Department of CSE, Bangalore, India; Bijeesh T.V., CHRIST University, Department of CSE, Bangalore, India; Bejoy B.J., CHRIST University, Department of CSE, Bangalore, India Description An account of the resource Alzheimer's disease (AD) remains shrouded in mystery, with its early detection posing a significant challenge. This research paper delves into the cutting-edge realm of deep learning, exploring its potential to explore the brain's secrets and revolutionize AD diagnostics using Magnetic Resonance Imaging (MRI) data. Upon comprehensively reviewing the performance of six state-of-the-art models and studying their strengths and limitations on MRI data, this paper proposes a novel deep-learning architecture based on the InceptionV3 model for Alzheimer's Disease prediction using MRI data. The proposed architecture leverages convolutional neural networks (CNNs) to extract subtle brain structure and function patterns, potentially identifying early AD signatures before noticeable cognitive decline. The proposed model is validated on a large-scale MRI dataset that comprises four stages of dementia, demonstrating more insights. Inception V3 base model yielded 82% accuracy, measured using the metric Area Under the Curve (AUC), on the dataset, and an improved AUC of 87% was achieved by performing data augmentation to remove the class imbalance in the dataset. The proposed deep learning model built on top of Inception V3 exhibited an improved performance with an AUC of 88% underlining the potential of deep learning models in early AD detection. The paper's findings will contribute to the ongoing effort to revolutionize AD diagnosis and accelerate the development of personalized treatment strategies. Grenze Scientific Society, 2025. Subject The topic of the resource Alzheimer's disease detection; Convolutional Neural Networks; Deep Learning; Magnetic Resonance Imaging (MRI) data Publisher An entity responsible for making the resource available Grenze Scientific Society 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