Early and accurate diagnosis of life-threatening pulmonary diseases like tuberculosis and lung cancer is essential for reducing mortality rates, yet remains challenging. This paper addresses these challenges by proposing a novel deep learning-based segmentation model, EC-CBAM-UNET, which significantly improves the foundational preprocessing step of precise lung field segmentation. The proposed architecture extends the classical U-Net by integrating a modified Convolutional Block Attention Module (CBAM), in which the channel attention component is replaced with an Efficient Channel Attention (ECA) mechanism while retaining the original spatial attention. This directs the network to focus on diagnostically significant regions while suppressing irrelevant features. Furthermore, Efficient Convolutional (EC) blocks replace standard convolutions in both paths, achieving computational efficiency. For hyperparameter tuning of this architecture a multi-objective optimization technique based on genetic algorithms is used. The resulting model is trained and evaluated on publicly available medical imaging dataset. Quantitative evaluation demonstrates that EC-CBAM-UNET achieves a Dice Similarity Coefficient (DSC) of 97.5%, Precision of 98.3%, and Recall of 96.8%, outperforming existing approaches. By providing high-fidelity lung masks, the proposed architecture delivers reliable inputs for downstream Computer-Aided Diagnosis (CAD) models.