Deep Learning by a Tensor Network Algorithm Provides Hyperfast Training of Business Finance

2020 ◽  
Author(s):  
Alfredo Lacayo Evertsz ◽  
Lizelia Bravo Boza
Keyword(s):  
Deep Learning ◽  
Network Algorithm ◽  
Tensor Network

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

scholarly journals Deep Learning-Based Image Feature with Arthroscopy-Aided Early Diagnosis and Treatment of Meniscus Injury of Knee Joint

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zijian Li ◽  
Shiyou Ren ◽  
Xintao Zhang ◽  
Lu Bai ◽  
Changqing Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Knee Joint ◽  
Knee Function ◽  
Partial Resection ◽  
Knee Meniscus ◽  
Meniscus Injury ◽  
Secondary Resection ◽  
Network Algorithm ◽  
Meniscus Suture ◽  
Mri Image

The aim of this study is to explore the clinical effect of deep learning-based MRI-assisted arthroscopy in the early treatment of knee meniscus sports injury. Based on convolutional neural network algorithm, Adam algorithm was introduced to optimize it, and the magnetic resonance imaging (MRI) image super-resolution reconstruction model (SRCNN) was established. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were compared between SRCNN and other algorithms. Sixty patients with meniscus injury of knee joint were studied. Arthroscopic surgery was performed according to the patients’ actual type of injury, and knee scores were evaluated for all patients. Then, postoperative scores and MRI results were analyzed. The results showed that the PSNR and SSIM values of the SRCNN algorithm were (42.19 ± 4.37) dB and 0.9951, respectively, which were significantly higher than those of other algorithms ( P  < 0.05). Among patients with meniscus injury, 17 cases (28.33%) were treated with meniscus suture, 39 cases (65.00%) underwent secondary resection, 3 cases (5.00%) underwent partial resection, and 1 case (1.67%) underwent full resection. After meniscus suture, secondary resection, partial resection, and total resection, the knee function scores of patients after treatment were (83.17 ± 8.63), (80.06 ± 7.96), (84.34 ± 7.74), and (85.52 ± 5.97), respectively. There was no great difference in knee function scores after different methods of treatment ( P  > 0.05), and there were considerable differences compared with those before treatment ( P  < 0.01). Compared with the results of arthroscopy, there was no significant difference in the grading of meniscus injury by MRI ( P  > 0.05). To sum up, the SRCNN algorithm based on the deep convolutional network algorithm improved the MRI image quality and the diagnosis of knee meniscus injuries. Arthroscopic knee surgery had good results and had great clinical application and promotion value.

A new growing pruning deep learning neural network algorithm (GP-DLNN)

2019 ◽  
Vol 32 (24) ◽  
pp. 18143-18159 ◽  
Author(s):  
Ryad Zemouri ◽  
Nabil Omri ◽  
Farhat Fnaiech ◽  
Noureddine Zerhouni ◽  
Nader Fnaiech
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Deep Learning Neural Network
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