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An essential stage in computer aided diagnosis of chest X rays is automatedlung segmentation. Due to rib cages and the unique modalities of each personslungs, it is essential to construct an effective automated lung segmentationmodel. This paper presents a reliable model for the segmentation of lungs inchest radiographs. Our model overcomes the challenges by learning to ignoreunimportant areas in the source Chest Radiograph and emphasize importantfeatures for lung segmentation. We evaluate our model on public datasets,Montgomery and Shenzhen. The proposed model has a DICE coefficient of 98.1percent which demonstrates the reliability of our model.

Improved lung segmentation based on U-Net architecture and morphological operations