Application of Deep Learning Algorithm in Cervical Cancer MRI Image Segmentation Based on Wireless Sensor

2019 ◽  
Vol 43 (6) ◽  
Author(s):  
Peng Liang ◽  
Guijun Sun ◽  
Sirong Wei
Keyword(s):  
Cervical Cancer ◽  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Deep Learning Algorithm ◽  
Mri Image

Ensemble-based deep meta learning for medical image segmentation

2021 ◽  
pp. 1-7
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
The State ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Meta Learning ◽  
Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

Evaluation and Analysis of Cardiovascular Function in Intensive Care Unit Patients by Ultrasound Image Segmentation Based on Deep Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 1892-1898
Author(s):  
Jiaqi Shen ◽  
Fangfang Huang ◽  
Myers Ulrich
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Carotid Artery ◽  
Blood Vessel ◽  
Nerve Cell ◽  
Clinical Application ◽  
Learning Algorithm ◽  
Linear Filter ◽  
Multi Scale ◽  
Deep Learning Algorithm

Many studies have shown that cardiovascular disease has become one of the major diseases leading to death in the world. Therefore, it is a very meaningful topic to use image segmentation technology to segment blood vessels for clinical application. In order to automatically extract the features of blood vessel images in the process of segmentation, the deep learning algorithm is combined with image segmentation technology to segment the nerve cell membrane and carotid artery images of ICU patients, and to segment the blood vessel images from a multi-dimensional perspective. The relevant data are collected to observe the effect of this model. The results show that the three-dimensional multi-scale linear filter has a good effect on carotid artery segmentation in the image segmentation of nerve cell membranes and carotid artery. When analyzing the accuracy of vascular image segmentation from network parameters and training parameters, it is found that the accuracy of the threedimensional multi-scale linear filter can reach about 85%. Therefore, it can be found that the combination of deep learning algorithm and image segmentation technology has a good segmentation effect, and the segmentation accuracy is also high. The experiment achieves the desired effect, which provides experimental basis for the clinical application of the vascular image segmentation technology.

scholarly journals Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lifang Sun ◽  
Xi Hu ◽  
Yutao Liu ◽  
Hengyu Cai
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Malignant Tumors ◽  
Image Features ◽  
Control Group ◽  
Deep Learning Algorithm ◽  
Image Characteristics ◽  
Traditional Algorithm ◽  
Mri Image

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.

scholarly journals A Moving Object Detection Method Using Deep Learning-Based Wireless Sensor Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Linghua Zhao ◽  
Zhihua Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Moving Target ◽  
Deep Learning Algorithm

Aiming at the problem of real-time detection and location of moving objects, the deep learning algorithm is used to detect moving objects in complex situations. In this paper, based on the deep learning algorithm of wireless sensor networks, a novel target motion detection method is proposed. This method uses the deep learning model to extract visual potential representation features through offline similarity function ranking learning and online model incremental update and uses the hierarchical clustering algorithm to achieve target detection and positioning; the low-precision histogram and high-precision histogram cascade the method which determines the correct position of the target and achieves the purpose of detecting the moving target. In order to verify the advantages and disadvantages of the deep learning algorithm compared with traditional moving object detection methods, a large number of comparative experiments are carried out, and the experimental results were analyzed qualitatively and quantitatively from a statistical perspective. The results show that, compared with the traditional methods, the deep learning algorithm based on the wireless sensor network proposed in this paper is more efficient. The detection and positioning method do not produce the error accumulation phenomenon and has significant advantages and robustness. The moving target can be accurately detected with a small computational cost.

scholarly journals Ultrasound Elastography under Deep Learning Algorithm to Analyze the Therapeutic Effect of Clustered Regularly Interspaced Short Palindromic Repeats Short Hairpin Ribonucleic Acid Nanoparticles on Cervical Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Minghui Li ◽  
Weiwei Li ◽  
Liang Zhao
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Therapeutic Effect ◽  
Drug Administration ◽  
Learning Algorithm ◽  
Ultrasound Elastography ◽  
Control Group ◽  
Intelligent Algorithm ◽  
Deep Learning Algorithm ◽  
Experimental Group

This study aimed to analyze the effect of the deep learning algorithm on ultrasound elastography on the treatment of cervical cancer with clustered regularly interspaced short palindromic repeats (CRISPR) short hairpin ribonucleic acid (shRNA) nanoparticles, aiming to provide a reference for the clinical application of deep learning to analyze the therapeutic effect of the disease. In this study, CRISPR and shRNA plasmid nanoparticle drugs were used to treat 55 patients with cervical cancer in the experimental group, and normal saline was injected to another 53 patients in the control group, so compare the effect of nanoparticles in the treatment of cervical cancer. Professional doctors and the recurrent neural network (RNN) intelligent algorithm were used to score cervical cancer based on the ultrasound elastograph images by taking blue, green, and red (BGR) as diagnosis criteria. As a result, the experimental group had a total of 217 points before drug administration and a total of 224 points after drug administration. Each patient had an average increase of 0.13 points. The control group had a total of 200 points before drug administration and a total of 223 points after drug administration, and each patient had an average increase of 0.43 points. The experimental group was obviously different from the control group ( P < 0.05 ). Each tissue image output by the RNN was clearer than the original image, and the score given by intelligent calculation was faster than that of professional doctors. The monitoring effect of the deep learning RNN intelligent algorithm on the therapeutic effect of nanomedicine was analyzed. It was found that the average accuracy of the experimental group and the control group was 98.95% and 90.34%, respectively; and the experimental group was greatly different from the control group ( P < 0.05 ). In short, nano-CRISPR and shRNA drugs had remarkable effects on the treatment of cervical cancer, and the scores given by the deep learning intelligent algorithm were faster and more accurate, which provided theoretical guidance for the clinical application of deep learning algorithms to analyze the treatment effects of diseases.

scholarly journals K-Clustering for the Recognition of Moving Entity by Wireless Sensor Networks Using Deep Learning

2021 ◽  
Author(s):  
Thiyagarajan R ◽  
Balajivijayan V ◽  
Rajalakshmi D
Keyword(s):  
Wireless Sensor Networks ◽  
Deep Learning ◽  
Sensor Networks ◽  
Learning Algorithm ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Deep Learning Algorithm ◽  
Positioning Method

Abstract For the detection of the moving entity, a deep learning algorithm is used. To detect the complex type of situations, real-time monitoring of a moving object has to be detected. Using the deep learning method, wireless sensor networks are diagnosed using virtual representation features. Using the k-clustering technique, it achieves the target detection and can determine the positioning. It can determine the characteristics based on the precision. The wireless sensor networks are proposed and analyzed by means of a statistical approach. Statistical way of clustering up of the data detects the precision rate and its positioning state. The positioning method eventually reduces the error which accumulates the efficiency. The main advantages are the robustness and efficiency to improve the performance. These moving targets can be detected with less computational cost. In the field of pervasive computing, the recognition of a moving target can be improvised. The sensor node transmits the information, i.e., communication to nodes. In this paper, the researches focus on wireless sensor data along with a moving entity. This research is mainly used in healthcare and AI-based applications. By using the IoT with wireless sensor networks, the detection of a moving entity can be determined by using the combination of k-clustering algorithms. This deep learning algorithm reduces the time complexity and determines integrity in data.

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