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.

Clinical Diagnostic Value of Magnetic Resonance Imaging in Knee Joint Sports Injury

2021 ◽  
Vol 11 (2) ◽  
pp. 453-461
Author(s):  
Bing Wang ◽  
Li Wang ◽  
Yingyi Wang ◽  
Fen Qin
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Knee Joint ◽  
Clinical Judgment ◽  
Lateral Meniscus ◽  
Clinical Effects ◽  
Resonance Imaging ◽  
Knee Meniscus ◽  
Meniscus Injury ◽  
Sensitivity Specificity

This article mainly analyzes the clinical effects of magnetic resonance diagnosis in knee meniscus injury. Patients with knee meniscus injury were taken as the research object. All patients used magnetic resonance examination and surgery and arthroscopy examination, and surgery and arthroscopy examination as the control parameters. The analysis used magnetic resonance diagnosis results and the classification of meniscus injury diagnosed by magnetic resonance and surgery and arthroscopy. The results showed that the sensitivity, specificity, and accuracy of conventional MR1 sequence diagnosis of medial meniscus injury were 86.3%, 95.6%, and 92.4%, respectively. The sensitivity, specificity and accuracy of conventional MRI in diagnosing lateral meniscus injury of the knee joint were 91.3%, 94.5%, and 92.5% respectively. The sensitivity of MRI to medial and lateral meniscus injury was (χ2 = 0.07, P > 0.77), There were no significant differences in specificity χ2 = 0.01, P > 0.77) and accuracy χ2 = 0.01, P > 0.77). The knee meniscus injury has a greater impact on patients. The diagnostic effect of magnetic resonance imaging is better and the diagnostic accuracy is higher. It can help clinical judgment and treatment. The effect is obvious and worthy of clinical promotion.

scholarly journals Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoxiao Xie ◽  
Zhen Li ◽  
Lu Bai ◽  
Ri Zhou ◽  
Canfeng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Tibial Plateau ◽  
Image Data ◽  
High Sensitivity ◽  
Tibial Plateau Fractures ◽  
Meniscus Injury ◽  
High Consistency ◽  
Sensitivity Specificity ◽  
Mri Image

This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.

A Novel Deep Learning Based Auto Segmentation For Rectum Tumor On MRI Image

2018 ◽  
Vol 102 (3) ◽  
pp. e548
Author(s):  
J. Wang ◽  
J. Lu ◽  
G. Qing ◽  
L. Shen ◽  
Y. Sun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mri Image

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.

Brain Tumor Detection using Hybrid Deep Learning with Genetic Algorithm

2021 ◽  
Vol 23 (09) ◽  
pp. 981-993
Author(s):  
T. Balamurugan ◽  
◽  
E. Gnanamanoharan ◽  
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Brain Tumor ◽  
Advanced Technology ◽  
Great Promise ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Feature Subset ◽  
Brain Tumor Segmentation ◽  
Mri Image

Brain tumor segmentation is a challenging task in the medical diagnosis. The primary aim of brain tumor segmentation is to produce precise characterizations of brain tumor areas using adequately placed masks. Deep learning techniques have shown great promise in recent years for solving various computer vision problems such as object detection, image classification, and semantic segmentation. Numerous deep learning-based approaches have been implemented to achieve excellent system performance in brain tumor segmentation. This article aims to comprehensively study the recently developed brain tumor segmentation technology based on deep learning in light of the most advanced technology and its performance. A genetic algorithm based on fuzzy C-means (FCM-GA) was used in this study to segment tumor regions from brain images. The input image is scaled to 256×256 during the preprocessing stage. FCM-GA segmented a preprocessed MRI image. This is a versatile advanced machine learning (ML) technique for locating objects in large datasets. The segmented image is then subjected to hybrid feature extraction (HFE) to improve the feature subset. To obtain the best feature value, Kernel Nearest Neighbor with a genetic algorithm (KNN-GA) is used in the feature selection process. The best feature value is fed into the RESNET classifier, which divides the MRI image into meningioma, glioma, and pituitary gland regions. Real-time data sets are used to validate the performance of the proposed hybrid method. The proposed method improves average classification accuracy by 7.99 % to existing Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) classification algorithms

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