scholarly journals Support Vector Machine-Based Backprojection Algorithm for Detection of Gastric Cancer Lesions with Abdominal Endoscope Using Magnetic Resonance Imaging Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huajia Dai ◽  
Yuhao Bian ◽  
Libin Wang ◽  
Junfeng Yang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Gastric Cancer ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Hospital Stay ◽  
Radical Resection ◽  
Support Vector ◽  
Control Group ◽  
Resonance Imaging ◽  
Experimental Group

This study was to analyze the diagnostic value of magnetic resonance imaging (MRI) for gastric cancer (GC) lesions and the treatment effect of complete laparoscopic radical resection (CLSRR). A malignant tumor recognition algorithm was constructed in this study based on the backprojection (BP) and support vector machine (SVM), which was named BPS. 78 GC patients were divided into an experimental group (received CLSRR) and a control group (received assisted laparoscopic radical resection (ALSRR)), with 39 cases in each group. It was found that the BPS algorithm showed lower relative mean square error (MSE) in axle x (OMSE, x) and axle y (OMSE, x), but the classification accuracy (CA) was the opposite ( P < 0.05 ). The postoperative hospital stay, analgesia duration, first exhaust time (FET), and first off-bed activity time (FOBA) for patients in the experimental group were less ( P < 0.05 ). The operation time of the experimental group (270.56 ± 90.55 min) was significantly longer than that of the control group (228.07 ± 75.26 min) ( P < 0.05 ). There were 3 cases of anastomotic fistula, 1 case of acute peritonitis, and 2 cases of lung infections in the experimental group, which were greatly less than those in the control group (7 cases, 4 cases, and 3 cases) ( P < 0.05 ). In short, the BPS algorithm was superior in processing MRI images and could improve the diagnostic effect of MRI images. The CLSRR could reduce the length of hospital stay and the probability of complications in GC patients, so it could be used as a surgical plan for the clinical treatment of advanced GC.

scholarly journals Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Functional Magnetic Resonance Imaging in Diagnosing Cerebral Ischemic Stroke

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kun Fan ◽  
Ting Zhang ◽  
Weihong He
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Cognitive Assessment ◽  
Intervention Group ◽  
Support Vector ◽  
Control Group ◽  
Resonance Imaging ◽  
Cerebral Ischemic Stroke ◽  
Cerebral Ischemic

This study was to explore the value of the blood oxygenation level dependent-functional magnetic resonance imaging (BOLD-fMRI) image classification based on the multilevel clustering-evolutionary random support vector machine cluster (MCRSVMC) algorithm in the diagnosis and treatment of patients with cognitive impairment after cerebral ischemic stroke (CIS). The MCRSVMC algorithm was optimized using a clustering algorithm, and it was compared with other algorithms in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPE) of classifying the brain area images. 36 patients with cognitive impairment after CIS and nondementia patients were divided into a control group (drug treatment) and an intervention group (drug + acupuncture) according to different treatment methods, with 18 cases in each group. The changes in regional homogeneity (ReHo) of BOLD-fMRI images and the differences in scores of the Montreal Cognitive Assessment Scale (MoCA), scores of Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), and scores of Functional Independence Measure (FIM) between the two groups of patients were compared before and after treatment. The results revealed that the average classification ACC, SEN, and SPE of the MCRSVMC algorithm were 84.25 ± 4.13%, 91.07 ± 3.51%, and 89 ± 3.96%, respectively, which were all obviously better than those of other algorithms ( P < 0.01 ). When the number of support vector machine (SVM) classifiers and the number of important features were 410 and 260, respectively, the classification ACC of MCRSVMC algorithm was 0.9429 and 0.9092, respectively. After treatment, the MoCA score, LOTCA score, and FIM score of the patients in the intervention group were higher than those of the control group ( P < 0.05 ). The ReHo values of the right inferior temporal gyrus and right inferior frontal gyrus of patients in the intervention group were much higher than those of the control group ( P < 0.05 ). It indicated that the classification ACC, SEN, and SPE of the magnetic resonance imaging (MRI) based on the MCRSVMC algorithm in this study were greatly improved, and the acupuncture method was more effective in the treatment of patients with cognitive dysfunction after CIS.

scholarly journals Patients With Acute Ischemic Stroke Who Receive Brain Magnetic Resonance Imaging Demonstrate Favorable In‐Hospital Outcomes

2020 ◽  
Vol 9 (20) ◽  
Author(s):  
Hwan Lee ◽  
Yifeng Yang ◽  
Baoqiong Liu ◽  
Simon A. Castro ◽  
Tiantian Shi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Ischemic Stroke ◽  
Magnetic Resonance ◽  
Hospital Stay ◽  
Acute Ischemic Stroke ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Control Group ◽  
Inpatient Mortality ◽  
Resonance Imaging

Background Use of inpatient brain magnetic resonance imaging (MRI) in patients with acute ischemic stroke is highly institution dependent and has been associated with increased length and cost of hospital stay. We examined whether inpatient brain MRI in patients with acute ischemic stroke is associated with improved clinical outcomes to justify its resource requirements. Methods and Results The National Inpatient Sample database was queried retrospectively to find 94 003 patients who were admitted for acute ischemic stroke and then received inpatient brain MRI between 2012 and 2014. Multivariable regression analysis was performed with respect to a control group to assess for differences in the rates of inpatient mortality and complications, as well as the length and cost of hospital stay based on brain MRI use. Inpatient brain MRI was independently associated with lower rates of inpatient mortality (1.67% versus 3.09%; adjusted odds ratio [OR], 0.60; 95% CI, 0.53–0.68; P <0.001), gastrostomy (2.28% versus 2.89%; adjusted OR, 0.82; 95% CI, 0.73–0.93; P <0.001), and mechanical ventilation (1.97% versus 2.82%; adjusted OR, 0.68; 95% CI, 0.60–0.77; P <0.001). Brain MRI was independently associated with ≈0.32 days (8%) and $1131 (11%) increase in the total length ( P <0.001) and cost ( P <0.001) of hospital stay, respectively. Conclusions Inpatient brain MRI in patients with acute ischemic stroke is associated with substantial decrease in the rates of inpatient mortality and complications, at the expense of marginally increased length and cost of hospitalization.

scholarly journals Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging Images in Analysis of Therapeutic Effects on Cerebral Ischemic Stroke

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhili Zhang ◽  
Guo Cheng ◽  
Guifang Liu ◽  
Gaixia Li
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Motor Area ◽  
Support Vector ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Activation Rate ◽  
Sensory Motor

The study aimed to explore the relationship between cerebral ischemic stroke (CIS) and the patient’s limb movement through the blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) based on multilevel clustering-evolutionary random support vector machine cluster (MCRSVMC). Specifically, 20 CIS patients were defined as the experimental group; another 20 healthy volunteers were defined as the control group. All subjects performed finger movement and verb association task. The performance of support vector machine (SVM) and MCRSVMC algorithm was compared and applied to functional magnetic resonance imaging (fMRI) of blood oxygen level in all subjects. The results showed that the average accuracy of MCRSVMC algorithm was significantly higher than that of support vector machine (86.75%, 65.84%; P < 0.05 ). The sensitivity of MCRSVMC algorithm was significantly higher than that of support vector machine (92.52%, 75.41%; P < 0.05 ). In addition, the specificity of MCRSVMC algorithm was significantly higher than that of support vector machine (86.39%, 68.24%; P < 0.05 ). When CIS patients performed finger exercise, the sensory motor areas on both sides were significantly activated, and the activated sensory motor areas on both sides were significantly bigger than the ipsilateral area. The activation rate of the left-sensory motor area (L-SM1) was 87.5%, the activation rate of the right-sensory motor area (R-SM1) was 25%, the activation rate of the left-side auxiliary motor area (L-SMA) was 62.5%, and the activation rate of the right-side auxiliary motor area (R-SMA) was 37.5%. In conclusion, the MCRSVMC algorithm proposed in this study is highly efficient and stable. BOLD-fMRI diagnosis of motor function in CIS patients is mainly related to compensation around the lesion, which occurs on the healthy side after recovery.

scholarly journals Magnetic Resonance Imaging Features under Deep Learning Algorithms in Evaluated Cerebral Protection of Craniotomy Evacuation of Hematoma under Propofol Anesthesia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Deqian Xin ◽  
Zhongzhe An ◽  
Juan Ding ◽  
Zhi Li ◽  
Leyan Qiao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Super Resolution ◽  
Control Group ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Resolution Algorithm ◽  
Experimental Group

This study aimed to explore the value of magnetic resonance imaging (MRI) features based on deep learning super-resolution algorithms in evaluating the value of propofol anesthesia for brain protection of patients undergoing craniotomy evacuation of the hematoma. An optimized super-resolution algorithm was obtained through the multiscale network reconstruction model based on the traditional algorithm. A total of 100 patients undergoing craniotomy evacuation of hematoma were recruited and rolled into sevoflurane control group and propofol experimental group. Both were evaluated using diffusion tensor imaging (DTI) images based on deep learning super-resolution algorithms. The results showed that the fractional anisotropic image (FA) value of the hind limb corticospinal tract of the affected side of the internal capsule of the experimental group after the operation was 0.67 ± 0.28. The National Institute of Health Stroke Scale (NIHSS) score was 6.14 ± 3.29. The oxygen saturation in jugular venous (SjvO2) at T4 and T5 was 61.93 ± 6.58% and 59.38 ± 6.2%, respectively, and cerebral oxygen uptake rate (CO2ER) was 31.12 ± 6.07% and 35.83 ± 7.91%, respectively. The difference in jugular venous oxygen (Da-jvO2) at T3, T4, and T5 was 63.28 ± 10.15 mL/dL, 64.89 ± 13.11 mL/dL, and 66.03 ± 11.78 mL/dL, respectively. The neuron-specific enolase (NSE) and central-nerve-specific protein (S100β) levels at T5 were 53.85 ± 12.31 ng/mL and 7.49 ± 3.16 ng/mL, respectively. In terms of the number of postoperative complications, the patients in the experimental group were better than the control group under sevoflurane anesthesia, and the differences were substantial ( P  < 0.05). In conclusion, MRI images based on deep learning super-resolution algorithm have great clinical value in evaluating the degree of brain injury in patients anesthetized with propofol and the protective effect of propofol on brain nerves.

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