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Random noise suppression of seismic data through multi-scale residual dense network

Gao Lei, Zhao KeCe, Min Fan, Bai Si-Chang

Acta Geophysica

2023

Vol. 71, no. 2

637--647

Random noise suppression is an important technique to improve the efficiency and accuracy of seismic data processing. Physical denoising methods such as f − x deconvolution and K-SVD have been widely adopted by the industry, while popular learning-based methods such as neural networks have emerged as good alternatives. In this paper, we propose a multi-scale residual dense network (MSRDN) for random noise suppression of seismic raw data. First, the network consists of a shallow feature extraction module, multiple residual blocks and multiple up-sampling modules. They are used for feature extraction, noise learning and size restoration. Second, each residual block is composed of multiple dense blocks. They are designed to alleviate network degradation. Third, dense blocks are tightly connected by multi-scale convolutional layers. They can enhance the regularization effect of the network. The experimental results show that MSRDN is more accurate and stable than previous algorithms.

COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images

Fang Lingling, Wang Xin

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

977--994

Corona virus disease 2019 (COVID-19) testing relies on traditional screening methods, which require a lot of manpower and material resources. Recently, to effectively reduce the damage caused by radiation and enhance effectiveness, deep learning of classifying COVID-19 negative and positive using the mixed dataset by CT and X-rays images have achieved remarkable research results. However, the details presented on CT and X-ray images have pathological diversity and similarity features, thus increasing the difficulty for physicians to judge specific cases. On this basis, this paper proposes a novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. To solve the problem of feature similarity between lung diseases and COVID-19, the extracted features are enhanced by an adaptive region enhancement algorithm. Besides, the depth network based on the residual blocks and the dense blocks is trained and tested. On the one hand, the residual blocks effectively improve the accuracy of the model and the non-linear COVID-19 features are obtained by cross-layer link. On the other hand, the dense blocks effectively improve the robustness of the model by connecting local and abstract information. On mixed X-ray and CT datasets, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and accuracy can all reach 0.99. On the basis of respecting patient privacy and ethics, the proposed algorithm using the mixed dataset from real cases can effectively assist doctors in performing the accurate COVID-19 negative and positive classification to determine the infection status of patients.