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Turkish Chest X-Ray Report Generation Model Using the Swin Enhanced Yield Transformer (Model-SEY) Framework.

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This summary is machine-generated.

This study introduces Model-SEY, an AI system for generating Turkish medical reports from chest X-rays. The deep learning model aims to improve diagnostic accuracy and reduce errors in clinical decision-making.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Natural Language Processing

Background:

  • Automated medical report generation from chest X-rays is challenging.
  • A significant gap exists in Turkish-language automatic reporting systems.
  • Accurate interpretation of medical images is crucial for clinical decisions.

Purpose of the Study:

  • To develop an AI model for automatic generation of Turkish medical reports from chest X-ray images.
  • To address the unmet need for Turkish-language medical reporting systems.
  • To enhance clinical decision-making support.

Main Methods:

  • A deep learning model, Model-SEY, was developed.
  • Swin Transformer was used for image feature extraction.
  • CosmosGPT architecture, adapted for Turkish, handled text generation.

Main Results:

  • A new dataset was created from Elazıg Fethi Sekin City Hospital and Indiana University.
  • Model-SEY achieved scores of 0.6412 (BLEU-1) and 0.2240 (ROUGE).
  • Generated reports were found to be meaningful and consistent.

Conclusions:

  • Model-SEY represents a pioneering effort in Turkish deep learning-based autonomous medical reporting.
  • The system shows potential to reduce diagnostic errors and aid physicians.
  • This work advances AI applications in medical imaging for the Turkish language.