The application of coagulatory neon networks in medicine for the detection of ischemic heart diseases and their development prospects


  • Solomatin Vladyslav
  • Baibuz Oleh
  • Syrotenko Andrii



Neural network, artificial intelligence, coronary heart disease, graph, Google Colab, Tensorflow, ResNet-50.


The analysis of recent research and publications is an important component of scientific investigation as it allows evaluating the current state of the scientific field and identifying the latest advancements and research directions. In the context of convolutional neural networks for detecting ischemic heart diseases on X-ray images, the analysis of recent research can help identify relevant techniques, methods, and findings that have already been achieved in this field. Recent research shows significant progress in the utilization of convolutional neural networks for medical diagnostic tasks. Many studies focus on the development and improve-ment of network architectures that can detect various pathological conditions of the heart on X-ray images. Additionally, research is being conducted on data processing methods, data augmentation techniques, and model training optimization to achieve better results. Publications dedicated to this topic provide information about various approaches for detecting ischemic heart diseases, the use of different network architectures, datasets, train-ing methods, and result evaluation. It is important to analyze these publications, assess their contributions to the field, identify the advantages and limitations of different approaches, and explore possibilities for further improvement. The gathered information from recent research and publications will serve as a founda-tion for the development of our article and the selection of an optimal approach towards de-veloping a convolutional neural network for detecting ischemic heart diseases on X-ray im-ages.


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