Hybrid Deep Learning Technique for Detecting Cardiovascular Diseases

2026
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Authors: R. SenthilPrabha; S. Yaswanthraj
Published in: Smart System for Integrated Computing and Communication
Publisher: Springer Nature Switzerland

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Abstract

This chapter explores the critical need for early detection of cardiovascular diseases, which are a leading cause of global mortality. The study introduces a hybrid deep learning technique that combines DenseNet and U-Net architectures to improve the accuracy of medical image segmentation. Key topics include the evaluation of cardiac viability using advanced imaging technologies, the application of machine learning algorithms for heart disease prediction, and the integration of real-world medical datasets to enhance diagnostic accuracy. The proposed model achieves high accuracy and recall, outperforming traditional methods. The results highlight the potential of this hybrid approach to revolutionize the diagnosis and treatment of heart disease, ultimately improving patient outcomes and reducing mortality rates.

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Metadata
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Title: Hybrid Deep Learning Technique for Detecting Cardiovascular Diseases
Authors: R. SenthilPrabha
S. Yaswanthraj
Publisher: Springer Nature Switzerland
Book: Smart System for Integrated Computing and Communication
Print ISBN: 978-3-031-99938-3

Electronic ISBN: 978-3-031-99939-0

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-031-99939-0_19

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