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An Integrated Framework for Early Detection of Diabetic Cardiomyopathy Using CARDIO-VGTS-Net Model

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Punganuru Swathi
M. Gunasekaran
Published in
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering
Publisher
Springer Nature Singapore

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Abstract

This chapter explores the development and application of the CARDIO-VGTS-Net framework for the early detection of Diabetic Cardiomyopathy (DCM). The study highlights the limitations of traditional diagnostic methods and introduces an advanced approach that combines the VGG-19 Convolutional Neural Network (CNN) and the Gray-Level Co-occurrence Matrix (GLCM) for enhanced image analysis. The methodology involves preprocessing microscopic images of blood vessels, enhancing contrast, and segmenting regions of interest using Kapur Thresholding and Canny edge detection. Features extracted from GLCM and VGG-19 are fed into a Support Vector Machine (SVM) classifier to detect coronary artery plaque and assess DCM severity. The experimental investigations demonstrate the effectiveness of the CARDIO-VGTS-Net model in improving diagnostic accuracy and providing timely interventions for better patient care. The conclusion emphasizes the potential of combining sophisticated image analysis techniques to improve diagnostic accuracy and patient outcomes.
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Title
An Integrated Framework for Early Detection of Diabetic Cardiomyopathy Using CARDIO-VGTS-Net Model
Authors
Punganuru Swathi
M. Gunasekaran
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering

Print ISBN: 978-981-9502-68-4

Electronic ISBN: 978-981-9502-69-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0269-1_98
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