Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Detection Of Cardiovascular Diseases With ECG Images Using Ml And Deep Learning

Authors
M. D. Kamalesh1, *, K. Hashvi1, M. Harshini1
1Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India
*Corresponding author. Email: kamalesh.cse@sathyabama.ac.in
Corresponding Author
M. D. Kamalesh
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_22How to use a DOI?
Keywords
ECG classification; deep learning; CNN-ResNet; Flask; TensorFlow; OpenCV; telemedicine
Abstract

This work presents a web-based intelligent diagnostic cardiovascular diseases automatic classification system based on the electrocardiogram (ECG) images. The system has a pre-trained hybrid CNNResNet deep learning model to assign the ECG images to four categories, Myocardial Infarction, History of Myocardial Infarction, Abnormal Heartbeat, and Normal. OpenCV is used to process images by resizing, color-space conversion, and normalization to obtain unchanged input to be used during model inference.

An implementation framework based on Flask is able to allow real-time ECG image uploading, user authentication, and delivery of predictions. The model provides softmax-based outputs which are products of probability to be applied in clinical interpretation. The proposed system shows an applicable combination of deep learning inference, preprocessing, and web deployment to have available cardiovascular screening and telemedicine interventions.

The proposed system illustrates a viable combination of deep learning and web implementation to provide cardiovascular screening and telemedicine systems without any obstacles.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_22How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. D. Kamalesh
AU  - K. Hashvi
AU  - M. Harshini
PY  - 2026
DA  - 2026/06/16
TI  - Detection Of Cardiovascular Diseases With ECG Images Using Ml And Deep Learning
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 207
EP  - 216
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_22
DO  - 10.2991/978-94-6239-693-7_22
ID  - Kamalesh2026
ER  -