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 Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging Subject The topic of the resource Acdc dataset; Cardiovascular disease; Deep learning; Medical imaging; Transfer learning Description An account of the resource Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images. 2022 Sibu Cyriac, Sivakumar R. and Nidhin Raju. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Creator An entity primarily responsible for making the resource Cyriac S.; Sivakumar R.; Raju N. Source A related resource from which the described resource is derived Journal of Computer Science, Vol-18, No. 11, pp. 1005-1020. Publisher An entity responsible for making the resource available Science Publications Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3844/JCSSP.2022.1005.1020" target="_blank" rel="noreferrer noopener">https://doi.org/10.3844/JCSSP.2022.1005.1020</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143831274&amp;doi=10.3844%2FJCSSP.2022.1005.1020&amp;partnerID=40&amp;md5=30291dd72e1e0cbcdb59a2f2f38dc225" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143831274&amp;doi=10.3844%2fJCSSP.2022.1005.1020&amp;partnerID=40&amp;md5=30291dd72e1e0cbcdb59a2f2f38dc225</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15493636 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 Cyriac S., Department of Computer Science, CHRIST (Deemed to be University), India; Sivakumar R., Department of Computer Science, CHRIST (Deemed to be University), India; Raju N., Centre for Digital Innovation, CHRIST (Deemed to be University), India