Classification of Brain Tumor on Magnetic Resonance Imaging Using Support Vector Machine

2021 ◽  
Author(s):  
Uswatun Hasanah ◽  
Riyanto Sigit ◽  
Tri Harsono
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Support Vector ◽  
Resonance Imaging

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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 Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

2020 ◽  
Vol 16 (12) ◽  
pp. 116
Author(s):  
Hamed Samadi Ghoushchi ◽  
Yaghoub Pourasad
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Support Vector ◽  
Statistical Features ◽  
Resonance Imaging ◽  
The Brain

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>

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