| This model for **lung segmentation in chest X-ray images** is based on a custom U-Net architecture enhanced with: | |
| - **ASPP (Atrous Spatial Pyramid Pooling)** in the bottleneck to capture multi-scale context and anatomical structures of varying size | |
| - **SE (Squeeze-and-Excitation) blocks** to enhance channel-wise attention and suppress irrelevant features such as ribs or background noise | |
| - **Dilated convolutions** in the decoder to increase the receptive field without sacrificing spatial resolution | |
| The model was trained on the [COVID-19 Radiography Database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database) and evaluated on a dedicated internal validation set. | |
| It achieves a **Dice score of 98.7%**, demonstrating good performance in segmenting lung fields. | |