scholarly journals Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia

2020 ◽  
Vol 14 (11) ◽  
pp. 2610-2615
Author(s):  
Yunsong Zheng ◽  
Hangbin Tong ◽  
Teng Zhao ◽  
Xiaoxia Guo ◽  
Hui Xu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Support Vector ◽  
Resonance Imaging ◽  
Support Vector Machine Classification

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 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 Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM)

2019 ◽  
Author(s):  
S HosseiniPanah ◽  
A Zamani ◽  
F Emadi ◽  
F HamtaeiPour
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
Support Vector ◽  
Morphological Operations ◽  
Resonance Imaging ◽  
Volume Number

Background: Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this disease over a period. Visualizing MS lesions in vivo with Magnetic Resonance Imaging (MRI) has a key role in observing the course of the disease.Material and Methods: Two different processing methods were present in this study in order to make an effort to detect and localize lesions in the patients’ FLAIR (Fluid-attenuated inversion recovery) images. Segmentation was performed using Ensemble Support Vector Machine (SVM) classification. The trained data was randomly divided into five equal sections, and each section was fed into the computer as an input to one of the SVM classifiers that led to five different SVM structures.Results: To evaluate results of segmentation, some criteria have been investigated such as Dice, Jaccard, sensitivity, specificity, PPV and accuracy. Both modes of ESVM, including first and second ones have similar results. Dice criterion was satisfied much better with specialist’s work and it is observed that Dice average has 0.57±.15 and 0.6±.12 values in the first and second approach, respectively.Conclusion: An acceptable overlap between those results reported by the neurologist and the ones obtained from the automatic segmentation algorithm was reached using an appropriate pre-processing in the proposed algorithm. Post-processing analysis further reduced false positives using morphological operations and also improved the evaluation criteria, including sensitivity and positive predictive value.

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