ItemConference Paper

Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique

Title
Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique
Creator
James, Jimmi; Alapatt, Bosco Paul; George, Fr. Jossy P.; Gupta, Varuna
Description
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.
Source
Lecture Notes in Networks and Systems;Volume;1477 LNNS;pp.295-305
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
CardioAugmentNet; Cardiovascular diseases; Deep learning; ECG classification
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981968638-4;
Format
online
Language
English
Type
Conference paper

Citation

James, Jimmi; Alapatt, Bosco Paul; George, Fr. Jossy P.; Gupta, Varuna, “Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25612.

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