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 Articles Article Faculty Publications -Articles 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 Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0 Subject The topic of the resource Disease diagnosis; Explainable AI; Smart healthcare; Transfer learning; Vision transformer Description An account of the resource Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models. 2024 Creator An entity primarily responsible for making the resource Poonia R.C.; Al-Alshaikh H.A. Source A related resource from which the described resource is derived Computers in Biology and Medicine, Vol-179 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.compbiomed.2024.108874" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compbiomed.2024.108874</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198332504&amp;doi=10.1016%2Fj.compbiomed.2024.108874&amp;partnerID=40&amp;md5=0a3b47831442b56320d50d33e8e202c1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198332504&amp;doi=10.1016%2fj.compbiomed.2024.108874&amp;partnerID=40&amp;md5=0a3b47831442b56320d50d33e8e202c1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 104825; PubMed ID: 39013343; CODEN: CBMDA Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, 201003, India; Al-Alshaikh H.A., Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia