{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:59:33Z","timestamp":1781251173219,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) at Morgan State University","award":["2131307"],"award-info":[{"award-number":["2131307"]}]},{"name":"Center for Equitable Artificial Intelligence and Machine Learning Systems (CEAMLS) at Morgan State University","award":["1OT2OD032581-01"],"award-info":[{"award-number":["1OT2OD032581-01"]}]},{"name":"National Science Foundation (NSF)","award":["2131307"],"award-info":[{"award-number":["2131307"]}]},{"name":"National Science Foundation (NSF)","award":["1OT2OD032581-01"],"award-info":[{"award-number":["1OT2OD032581-01"]}]},{"name":"Office of the Director, National Institutes of Health (NIH) Common Fund","award":["2131307"],"award-info":[{"award-number":["2131307"]}]},{"name":"Office of the Director, National Institutes of Health (NIH) Common Fund","award":["1OT2OD032581-01"],"award-info":[{"award-number":["1OT2OD032581-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model\u2019s decisions quite challenging. This lack of transparency constitutes both a practical and an ethical issue. For the present study, it is a major drawback to the deployment of deep learning methods mandated with detecting patterns and prognosticating Alzheimer\u2019s disease. Many approaches presented in the AI and medical literature for overcoming this critical weakness are sometimes at the cost of sacrificing accuracy for interpretability. This study is an attempt at addressing this challenge and fostering transparency and reliability in AI-driven healthcare solutions. The study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer\u2019s disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). The experimental results show the stacking ensemble achieving a remarkable accuracy of 98.0% while the hard voting ensemble reached 97.0%. The findings present a valuable contribution to the growing field of explainable artificial intelligence (XAI) in medical imaging, helping end users and researchers to gain deep understanding of the backstory behind medical image dataset and deep learning model\u2019s decisions.<\/jats:p>","DOI":"10.3390\/a18030163","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T10:16:45Z","timestamp":1741861005000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer\u2019s Disease Detection"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5709-0218","authenticated-orcid":false,"given":"Opeyemi Taiwo","family":"Adeniran","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Blessing","family":"Ojeme","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3246-0035","authenticated-orcid":false,"given":"Temitope Ezekiel","family":"Ajibola","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2039-3075","authenticated-orcid":false,"given":"Ojonugwa Oluwafemi Ejiga","family":"Peter","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8848-1843","authenticated-orcid":false,"given":"Abiola Olayinka","family":"Ajala","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0405-9088","authenticated-orcid":false,"given":"Md Mahmudur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computer, Mathematical and Natural Sciences, Morgan State University, Baltimore, MD 21251, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3318-2851","authenticated-orcid":false,"given":"Fahmi","family":"Khalifa","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore MD 21251, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3708","DOI":"10.1002\/int.22856","article-title":"Mutliresolutional Ensemble PartialNet for Alzheimer Detection using Magnetic Resonance Imaging Data","volume":"37","author":"Razzak","year":"2022","journal-title":"Int. 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