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 James, Jimmi; Alapatt, Bosco Paul; George, Fr. Jossy P.; Gupta, Varuna Title A name given to the resource Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1477 LNNS;pp.295-305 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8639-1_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8639-1_24</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020765274?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020765274?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 James J., Christ University, Bengaluru, India; Alapatt B.P., Christ University, Bengaluru, India; George F.J.P., Christ University, Bengaluru, India; Gupta V., Christ University, Bengaluru, India Description An account of the resource Cardiovascular disease is the leading cause of death and mortality worldwide. Thus, early diagnosis of CVDs is crucial since the disease can be managed with optimal care. In the current study, we consider CardioAugmentNet, which is a CNN model augmented with data augmentation strategies for the classification of several cardiovascular pathologies in ECG images. A proposed method was designed to provide a robust algorithm for the detection of irregular heart rhythms, myocardial infarction and other cardiac diseases. The model is trained and tested on the dataset of ECG images from individuals with various prevalent cardiovascular diseases as well as normal hearts. Therefore, the CardioAugmentNet state-of-the-art model classifies different cardiac abnormalities with high accuracy, suggesting that it can be used in clinical practice. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource CardioAugmentNet; Cardiovascular diseases; Deep learning; ECG classification Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968638-4; 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