  We initially proposed a deep learning approach for foreign objects inpainting
in smartphone-camera captured chest radiographs utilizing the cheXphoto
dataset. Foreign objects which can significantly affect the quality of a
computer-aided diagnostic prediction are captured under various settings. In
this paper, we used multi-method to tackle both removal and inpainting chest
radiographs. Firstly, an object detection model is trained to separate the
foreign objects from the given image. Subsequently, the binary mask of each
object is extracted utilizing a segmentation model. Each pair of the binary
mask and the extracted object are then used for inpainting purposes. Finally,
the in-painted regions are now merged back to the original image, resulting in
a clean and non-foreign-object-existing output. To conclude, we achieved
state-of-the-art accuracy. The experimental results showed a new approach to
the possible applications of this method for chest X-ray images detection.
