Update model: lung-segmentation-gan

.gitattributes

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README.md

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+This model is a fine-tuned version of a custom U-Net architecture enhanced with ASPP(Atrous Spatial Pyramid Pooling), Squeeze-and-Excitation blocks, and dilated convolutions.
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+It is designed for **semantic segmentation of lung regions in chest X-ray images**.
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+The model was further trained on a curated set of difficult X-ray examples with low contrast, overlapping anatomical structures, or weak/incomplete ground truth annotations.
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+To improve structural realism, the model was fine-tuned using a PatchGAN discriminator in an adversarial training setup.
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+This encouraged the generation of sharper, more anatomically consistent masks, especially on noisy or edge-case images.  
+The model also outperforms the original ground-truth masks on visual quality and edge precision.
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+It was trained on the publicly available [COVID-19 Radiography Database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database).
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+The final model achieves a **Dice score of 95.9%** on the internal validation set.

model.keras

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