Figure 3.

An example of advantages of the ensemble method for different quality of CXRs. The first to last row in each column shows an input CXR (a,b,c-1), a ground truth mask (a,b,c-2), an ensemble result (a,b,c-3), and five results predicted by the first to fifth model. (a-1) A clear CXR that shows none of severe noise from a portable device and obstacles like medical devices, (b) a lung mask of (a-1,a-3) an ensemble mask from the first to the fifth masks (a-4–a-8). Dice coefficients of (a-3–a-8) are 0.955, 0.928, 0912, 0.948, 0.948, and 0.948, respectively. (i) An CXR showing severe blurry within both lung regions due to lung opacity, (b-2) a lung mask of (b-1,b-3) an ensemble mask from the first to the fifth masks (b-4–b-8). Dice coefficients of (b-3–b-8) are 0.955, 0.928, 0912, 0.948, 0.948, and 0.948, respectively. (c-1) An CXR showing sever noise generated from a portable device, (c-2) a lung mask of (c-1,c-3) an ensemble mask from the first to the fifth masks (c-4–c-8). Dice coefficients of (c-3–c-8) are 0.899, 0.783, 0.885, 0.883, 0.879, and 0.903, respectively.