Turkish Chest X-Ray Report Generation Model Using the Swin Enhanced Yield Transformer (Model-SEY) Framework
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View abstract on PubMed
Summary
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.
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.
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