{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:50:13Z","timestamp":1792799413456,"version":"4.5.0"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"crossref","award":["PNURSP2026R850"],"award-info":[{"award-number":["PNURSP2026R850"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background\/Objectives: Alzheimer\u2019s disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI). Methods: Four ML classifiers\u2014K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)\u2014were trained on demographic and clinical features. For stage-wise classification, five DL models\u2014CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2\u2014were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations. Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement. Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.<\/jats:p>","DOI":"10.3390\/tomography12010004","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T08:38:14Z","timestamp":1766738294000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Detection and Classification of Alzheimer\u2019s Disease Using Deep and Machine Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3517-0746","authenticated-orcid":false,"given":"Muhammad Zaeem","family":"Khalid","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of Engineering and Technology Lahore, New Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nida","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Engineering and Technology Lahore, New Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1798-0474","authenticated-orcid":false,"given":"Babar","family":"Ali","sequence":"additional","affiliation":[{"name":"University Institute of Radiological Sciences and Medical Imaging Technology, The University of Lahore, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jawwad Sami Ur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Riphah International University, Islamabad 45210, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saman","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Physics, University of the Punjab, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8260-4083","authenticated-orcid":false,"given":"Lama","family":"Almudaimeegh","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2895-2284","authenticated-orcid":false,"given":"Zuhal Y.","family":"Hamd","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7320-2795","authenticated-orcid":false,"given":"Awadia","family":"Gareeballah","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, College of Applied Medical Sciences, Taibah University, Al-Madinah Al-Monawara 42353, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s13195-022-00985-x","article-title":"A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer\u2019s disease","volume":"14","author":"Feng","year":"2022","journal-title":"Alzheimer\u2019s Res. 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