Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
This paper proposes a model algorithm based on convolutional neural network combined with attention mechanism to realize fast and accurate identification of biological image. Firstly, deformable convolution is used to extract features in the horizontal and vertical directions, respectively. Secondly, attention modules are used to capture remote dependencies in one spatial direction, while accurate position information is retained in another spatial direction, so that information in both vertical and horizontal directions can be retained; after a series of transformations, the attention vector is obtained and multiplied back to the original feature vector as a weight factor. The experimental results show that the proposed algorithm can effectively improve the image quality, improve the image clarity, avoid color distortion, and achieve good results in both synthetic and real low-illumination images, and the subjective and objective evaluation indicators are better than the contrast algorithm....
Mapping from remote sensing has become more effective in the field of geology, mainly in lithological discrimination and identification of hydrothermal alteration zones. The use of this technique consists in obtaining information about the rock mass and the main ones existing in the inaccessible areas. Satellite data from the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) sensor represent a favorable potential for detecting the spectral signatures of mineral zones and identifying their nature. These data are more reliable in places where the climate is arid with less abundant vegetation, as at the Oumjrane-Boukerzia mining district. This region which is part of the Eastern Anti-Atlas, is composed of several mineralized veins which still require detailed studies and exploration by the technique of remote sensing. In this work we applied several processing techniques on ASTER imagery such as Colored Composition, Principal Component Analysis and Ratio Bands. The use of the reports of the specialized Bands makes it possible to identify some hydrothermal alteration minerals within the mining district of Oumjrane Boukerzia. These minerals are represented mainly by iron oxides and hydroxides (Hematite, jarosite, limonite and goethite), carbonate minerals (dolomite, calcite), clay minerals (Illite, kaolinite and chlorite) and quartz minerals. This work allows us to produce a map of hydrothermal alteration zones which can be used as a valuable reference in the strategy of mining exploration for the base metals (Cu, Pb, Zn and Ba), in the mining district of Oumjrane-Boukerzia and in the entire Eastern Anti-Atlas....
With deep learning being widely used in various research fields, it is introduced into the research and analysis of multimedia data processing technology and application. First, the flow of multimedia data processing, the development of multimedia data, and the realization of multimedia data processing technology are explained and analyzed. Then, the related network results of deep learning (convolution network structure and countermeasure neural network structure) are put forward, and the image comparison of the activation function and the loss function of deep learning is analyzed, which provides functional algorithm support for the experimental analysis of deep learning in multimedia data processing technology. Finally, through the analysis of experimental data, it is concluded that deep learning has stronger advantages in the application research of multimedia data processing technology compared with other learning methods. In the multimedia data processing, the multimedia data processing technology is obviously superior to the data mining technology and data compression technology. Finally, under the support of deep learning data, we conclude that multimedia data processing technology is widely used and quoted in various fields. Therefore, with the development of multimedia, the amount of multimedia data is increasing; so, we should vigorously develop multimedia data processing technology in an all-round way....
With the rapid development of deep learning in recent years, it has shown excellent performance in various image and video processing tasks. In addition, it also has a great role in promoting the spatio-temporal fusion of remote sensing images. The reconstructed image can give people a good visual experience. The invention relates to a remote sensing image fusion method based on a progressive cascade deep residual network and provides an end-to-end progressive cascade deep residual network model for remote sensing image fusion. The use of the MSE loss function may cause oversmoothing of the fused image, so a new joint loss function is defined to capture finer spatial information to improve the spatial resolution of the fused image. Resize-convolution is used to replace the transposed convolution to eliminate the checkerboard effect in the fused image caused by the transposed convolution. Through the experiments on the remote sensing image fusion simulation and real datasets of multiple satellites, the data results of the proposed algorithm are more than 5.25% better than those of the comparative algorithm in the average quantification. The calculation time and system resource occupation are also reduced, which has important theoretical significance and application value in the field of artificial intelligence and image processing. It will play a certain role in promoting the theoretical research and application of remote sensing image fusion....
Background. Pressure injuries (PIs) impose a substantial burden on patients, caregivers, and healthcare systems, affecting an estimated 3 million Americans and costing nearly $18 billion annually. Accurate pressure injury staging remains clinically challenging. Over the last decade, object detection and semantic segmentation have evolved quickly with new methods invented and new application areas emerging. Simultaneous object detection and segmentation paved the way to segment and classify anatomical structures. In this study, we utilize the Mask-R-CNN algorithm for segmentation and classification of stage 1-4 pressure injuries. Methods. Images from the eKare Inc. pressure injury wound data repository were segmented and classified manually by two study authors with medical training. The Mask-R-CNN model was implemented using the Keras deep learning and TensorFlow libraries with Python. We split 969 pressure injury images into training (87.5%) and validation (12.5%) subsets for Mask-R-CNN training. Results. We included 121 random pressure injury images in our test set. The Mask- R-CNN model showed overall classification accuracy of 92.6%, and the segmentation demonstrated 93.0% accuracy. Our F1 scores for stages 1-4 were 0.842, 0.947, 0.907, and 0.944, respectively. Our Dice coefficients for stages 1-4 were 0.92, 0.85, 0.93, and 0.91, respectively. Conclusions. Our Mask-R-CNN model provides levels of accuracy considerably greater than the average healthcare professional who works with pressure injury patients. This tool can be easily incorporated into the clinician’s workflow to aid in the hospital setting....
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