Application of the efficientnet model for detecting ischemic heart disease
DOI:
https://doi.org/10.34185/1562-9945-1-156-2025-18Keywords:
Neural network, artificial intelligence, ischemic heart disease, chest X-rays, Google Colab, TensorFlow, EfficientNet.Abstract
This article examines a neural network model that facilitates the early detection of is-chemic heart disease on chest X-rays, playing a crucial role in the diagnosis and treatment of cardiovascular diseases. The study explores the application of convolutional neural net-works(CNNs) for the automatic detection of ischemic heart disease on chest X-rays. A dataset containing chest X-rays annotated by an experienced cardiologist was used in the study. A CNN model was developed and trained to detect signs of ischemic heart disease based on chest X-rays. The model was trained on a small dataset (700 images) and tested on an independent set of test images. The research results demonstrated that the convolutional neural network effectively recognizes signs of ischemic heart disease on chest X-rays with high accuracy and reliability. This could significantly improve the capabilities for early diagnosis of ischemic heart disease and help clinicians make informed decisions regarding patient treatment. The findings of this study confirm the potential of using convolutional neural networks in medical diagnostics and represent a new step forward in the automated detection of is-chemic heart disease based on chest X-rays. This paves the way for improving the accuracy and efficiency of cardiovascular disease diagnostics and reducing the burden on medical per-sonnel.
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