AN INTELLIGENT MODEL FOR ASSESSING CARDIAC FUNCTION BASED ON MEDICAL IMAGES
Received: 2026-07-15 06:09:30
Published: 2026-04-18
Abstract
This study proposes a predictive model based on a CNN + LSTM architecture for the automatic assessment of cardiac functional parameters, particularly the left ventricular ejection fraction (LVEF), using echocardiographic video data. In the proposed approach, convolutional neural networks (CNN) are employed to extract spatial features, while long short-term memory (LSTM) networks are utilized to capture temporal dependencies.
During the preprocessing stage, noise reduction, normalization, segmentation, and key frame selection techniques were applied, significantly improving the model’s performance. Experimental results demonstrate that the proposed method achieves high accuracy and shows strong agreement with clinical measurements.
The developed model can serve as an effective tool for early detection of cardiovascular diseases and for automating the diagnostic process in clinical practice.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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