The X-ray images are considered to be an affordable means to diagnose the diseases especially the cardio vascular diseases. Chest X-rays are even examined by modern doctors in order to identify the disease and cure them accordingly. Due to opacified nature of the X-Ray images and different morphology of human body, the lung segmentation of human chest becomes very tedious. In this paper, we have introduced an approach to overcome this limitation and achieve the art-of-state lung segmentation despite of different anatomy and opacity. The approach involves division formula based on U-Net. The used the previously trained models MobileNetV2 and InceptionResNetV2. The result acquired shows the increase in the efficiency of segmentation by 2.5% with respect to Dice score and 2.37% with respect to IoU when compared it with the tradition U-Net.

U-Net, lung segmentation, chest X-Rays, biomedical images