Coronavirus disease2019 (COVID-19)has become a global pandemic. Many recognition approaches based on convolutional neural networks have been proposed for COVID-19 chest X-ray images. However, only a few of them make good use of the potential inter- and intra-relationships of feature maps. Considering the limitation mentioned above, this paper proposes an attention-based convolutional neural network, called PCXRNet, for diagnosis of pneumonia using chest X-ray images. To utilize the information from the channels of the feature maps, we added a novel condense attention module (CDSE) that comprised of two steps: condensation step and squeeze-excitation step. Unlike traditional channel attention modules, CDSE first downsamples the feature map channel by channel to condense the information, followed by the squeeze-excitation step, in which the channel weights are calculated. To make the model pay more attention to informative spatial parts in every feature map, we proposed a multi-convolution spatial attention module (MCSA). It reduces the number of parameters and introduces more nonlinearity. The CDSE and MCSA complement each other in series to tackle the problem of redundancy in feature maps and provide useful information from and between feature maps. We used the ChestXRay2017 dataset to explore the internal structure of PCXRNet, and the proposed network was applied to COVID-19 diagnosis. As a result, the network achieves an accuracy of 94.619%, recall of 94.753%, precision of 95.286%, and F1-score of 94.996% on the COVID-19 dataset.

Introduction: Convolutional neural networks are widely used in gesture recognition research, which employs surface electromyography. However, when processing surface electromyography data, current deep learning models still face challenges, such as insufficient effective feature extraction, poor performance in multi-gesture recognition, and low accuracy in recognizing sparse surface electromyography.Methods: To address these issues, this study proposed a multi-stream adaptive convolutional neural networks with residual modules (MSACNN-RM) for surface electromyography gesture recognition, which integrates multiple streams of convolutional neural networks, adaptive convolutional neural networks, and residual modules to enhance the model’s feature extraction and learning capabilities. This improves the model’s ability to extract and understand complex data patterns.Results: The experimental results demonstrated that the model achieved recognition accuracies of 98.24%, 93.52%, and 92.27% respectively on the Ninapro DB1, Ninapro DB2, and Ninapro DB4 datasets. Compared with other deep learning models, MSACNN-RM achieves higher accuracy compared to existing models.Discussion: The proposed model explores features of sparse sEMG signals by leveraging multi-stream convolution, the combination of adaptive convolution modules and ResNet blocks enhances the model’s ability of extracting crucial gesture features. In the future, in order to deal with differences in sEMG signals caused by variations among individuals, a universal multi-gesture recognition algorithm should be developed. Meanwhile, the model should focus on optimizing and streamlining the network to reduce computational load.

Rib fracture is the most common thoracic clinical trauma. Most patients have multiple different types of rib fracture regions, so accurate and rapid identification of all trauma regions is crucial for the treatment of rib fracture patients. In this study, a two-stage rib fracture recognition model based on nnU-Net is proposed. First, a deep learning segmentation model is trained to generate candidate rib fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to determine whether they are fractures or not. The results show that the two-stage deep learning model proposed in this study improves the accuracy of rib fracture recognition and reduces the false-positive and false-negative rates of rib fracture detection, which can better assist doctors in fracture region recognition.

In order to explore the research approach and hotspots in the field of medical image processing in China, the authors retrieved related literature in the field of medical image processing in China from 2009 to 2019 in CNKI database, and preprocessed the retrieved data. Then Citespace was used to analyze the retrieved data of 1363 documents from 2009 to 2019, the paper counted the basic situation of the article’s publication time, subject, and analyzed the high frequency keywords, cited literatures with high frequency. The results show that the domestic medical image processing field has a high degree of attention to image segmentation, registration, and fusion. Deep learning, convolutional neural networks and transfer learning have been applied to medical image processing in recent years. This paper revealed domestic research trends in medical image processing, with a view to providing reference for research in medical image processing.