Machine Learning-Based Analysis of Echocardiography Images for Cardiac Disease Diagnosis

Authors

  • Pankaj Negi Asst. Professor, Department of Mech. Engg. Graphic Era Hill University, Dehradun Uttarakhand 248002
  • Dany John Department of Cardiology, Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth “Deemed To Be University” Karad Malkapur, Karad (Dist. Satara), Maharashtra, India. PIN – 415539
  • Vrince Vimal Graphic Era Hill University; Adjunct Professor Graphic Era Deemed to be University, Dehradun, India. 248002
  • Versha Prasad Assistant Professor School of Health Sciences C. S. J. M. University Kanpur

Keywords:

Electrocardiogram, photoplethysmography, valvular heart diseases, image acquisition

Abstract

Echocardiography, often known as cardiac ultrasonography, is the imaging technique that evaluates heart function and structure that is used the most frequently and is easily accessible. Echocardiography is one of the most often used imaging tests because it does not expose the patient to the dangers associated with ionizing radiation, in addition to its portability, quick picture collection, high temporal resolution, and rapid image acquisition. Echocardiography is both essential and sufficient for the diagnosis of various cardiovascular disorders. This includes conditions ranging from heart failure to diseases that affect the heart's valves. The primary goal of this study is to build a Hyperparameter Tuned (HPT) Xception emulate using Adagrad optimizer with Class attention the layer and BiLSTM model for MV diagnosis and classification. This will be accomplished by classifying arrhythmia disease with electrocardiogram (ECG) and photoplethysmography (PPG) signals using a machine learning approach.

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Image processing techniques

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Published

01.07.2023

How to Cite

Negi, P. ., John, D. ., Vimal, V. ., & Prasad , V. . (2023). Machine Learning-Based Analysis of Echocardiography Images for Cardiac Disease Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 08–14. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/2923

Issue

Vol. 11 No. 7s (2023): Advancements in Machine Learning for Computer Science and Decision Support Systems

Section

Research Article

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