Adoption of combined detection technology of tumor markers via deep learning algorithm in diagnosis and prognosis of gallbladder carcinoma

2021 ◽  
Author(s):  
Yigang Chang ◽  
Qian Wu ◽  
Limin Chi ◽  
Huaying Huo ◽  
Qiang Li
Keyword(s):  
Deep Learning ◽  
Tumor Markers ◽  
Gallbladder Carcinoma ◽  
Learning Algorithm ◽  
Diagnosis And Prognosis ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Combined Detection

Progressive Image Recognition Method and Its Application in Security In-spection Machines

2020 ◽  
Vol 01 ◽  
Author(s):  
Wu Jianxing ◽  
Zeng Dexin ◽  
Ju Qiaodan ◽  
Chang Zixuan ◽  
Yu Hai
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Learning Algorithm ◽  
Local Information ◽  
Feature Mapping ◽  
Recognition Method ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Security Inspection ◽  
Security Inspection Equipment

Background:: Owing to the ability of a deep learning algorithm to identify objects and the related detection technology of security inspection equipment, in this paper, we propose a progressive object recognition method that con-siders local information of objects. Methods:: First, we construct an X-Base model by cascading multiple convolutions and pooling layers to obtain the feature mapping image. Moreover, we provide a “segmented convolution, unified recognition” strategy to detect the size of the objects. Results:: Experimental results show that this method can effectively identify the specifications of bags passing through the security inspection equipment. Compared with the traditional VGG and progressive VGG recognition methods, the pro-posed method achieves advantages in terms of efficiency and concurrency. Conclusion:: This study provides a method to gradually recognize objects and can potentially assist the operators to identify prohibited objects.

scholarly journals Study on a Risk Model and Target Detection for Prediction and Avoidance of Unmanned Environmental Hazard

2021 ◽  
Author(s):  
Chengqun Qiu ◽  
Yuan Zhong ◽  
Jie Ji ◽  
Shuai Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Target Detection ◽  
Environmental Risk ◽  
Risk Model ◽  
Learning Algorithm ◽  
Unmanned Vehicles ◽  
Deep Learning Algorithm ◽  
Detection Technology ◽  
Comparison Results

Abstract Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction. In the avoidance part, the conservative driving strategy based on speed restriction is adopted according to the results of risk prediction. Additionally, the core function is achieved through the target detection technology based on deep learning algorithm and the data conclusion based on deep learning method. Moreover, the location of bounding box is further optimized to improve the accuracy of SSD target detection method based on solving the problem of unbalanced sample categories. Software such as MATLAB and Carsim are applied in the system. From the comparison results of the simulations of unmanned vehicles with or without a system, it that the system can provide effective safety guarantee for unmanned driving.

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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