SkiDNet: Skip Image Denoising Network for X-Rays
Sandipan Dutta, Shaurya Chaturvedi, Swaraj Kumar, M. P. S. Bhatia · 2019
Medical imaging has evolved to become an essential tool for screening and diagnosing diseases, but they have certain limitations just like every other technology. X-rays, which is one of the most common radiological examinations, is not immune to imperfections. In this paper, we aim to tackle one such imperfection in X-rays, which is the presence of undesirable noises which causes aberrations in the output projections. This makes diagnosis and analysis difficult since such noises shroud the intricate details that these images contain. Distinctive denoising algorithms have been proposed in the past for a spectrum of vision datasets, but a very few of them are for X-rays. We introduce a new denoising network called SkiDNet, a deep learning approach using an encoder-decoder architecture with skip connections of varying length. The network has been trained on the NIH Chest X-Ray Dataset. With the unique properties injected by different types of connections, SkiDNet is able to surpass the performance of existing models. Furthermore, adopting a different approach to weight initialization and batch normalization makes the network more robust. Denoised X-rays obtained from the network were objectively evaluated using different metrics namely mean squared error, peak signal-to-noise ratio, and the structural similarity index.