A Multi modal Framework for Precision Analysis

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a summary:The background: This study proposes a multi-language model (VLM) that benefits from Siglip encoded and the GEMMA-3B converter to enhance the automatic examination of chronic tuberculosis (TB). By combining x -ray images with clinical data, the model deals with manual interpretation challenges, improving diagnostic consistency and ease of access, especially in resource restrictions.

Method: VLM structure combines the vision transformer (VIT) for optical coding and a text encode based on the transformer to treat the clinical context, such as the history of patients and treatment records. Attention mechanisms via the media correspond to the features of radiography with text information, while the Gemma-3B decoding unit creates comprehensive diagnostic reports. The model was previously trained on 5 million associated medical images and texts and seized using 100,000 chronic X -rays on TB.

Results: The model showed high resolution (94 percent) and a summons (94 percent) to detect major chronic tuberculosis diseases, including fibrosis, calcified granules tumor, and bronchial expansion. The scores of the curve (AUC) exceeded 0.93, and the intersection was on the values ​​of the Union (ION) higher than 0.91, which led to the validity of its effectiveness in discovering and localizing tuberculosis distortions.

Conclusion: VLM provides a strong and developed solution to diagnose chronic TB Automated TB, and to merge radiology and beds to provide implemented and context visible visions. Future work will treat precise diseases and database biases to enhance the generalization of the model, ensure fair performance across the various population and healthcare settings.