Neonatal hyperbilirubinemia is a disease of bilirubin metabolism disorder, which is a common in newborns. Without timely medical attention, neonatal hyperbilirubinemia may develop into acute bilirubin encephalopathy, resulting in serious long-term neurological deficits. Magnetic resonance imaging, as a non-invasive imaging technique, is widely used in the diagnosis of acute bilirubin encephalopathy in newborns. However, the T1-weighted images of magnetic resonance imaging of newborns with normal myelin development and newborns with acute bilirubin encephalopathy have similar high signal intensity, making it difficult to make a clinical diagnosis based on the conventional radiological reading. As an important computer-aided diagnosis method, deep convolutional neural network has been widely used to improve the work efficiency of radiologists. In this paper, a convolutional neural network based on classification network for acute bilirubin encephalopathy is proposed. It contains a feature fusion section and a fairly deep Resnet classification network. Experimental results show that the performance of the proposal is better than those of deep learning models in discussion.
Due to the impact of Corona Virus Disease 2019 (COVID-19), facial mask has become a necessary protective measure for people going out in the last two years. One's mouth and nose are covered to suppress the spread of the virus, which brings a huge challenge for face verification. Whereas some existing image inpainting methods cannot repair the covered area well, which reduces the accuracy of face verification. In this paper, an algorithm is proposed to repair the area covered by facial mask to restore the identity information for face authentication. The proposed algorithm consists of an image inpainting network and a face verification network. Among them, in image inpainting network, to begin with, two discriminators, namely global discriminator and local discriminator. Then Resnet blocks are employed in two discriminators, which is used to retain more feature information. Experimental results show that the proposed method generates fewer artifacts and receives the higher Rank-1 accuracy than other methods in discussion.
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