A novel approach for the brain tumor detection and classification using support vector machine

2017 ◽  
Author(s):  
B. B. Shankaragowda ◽  
M. Siddappa ◽  
M. Suresha
Keyword(s):  
Support Vector Machine ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Support Vector ◽  
Novel Approach ◽  
The Brain

scholarly journals An Automated Method for MRI Based Brain Tumor Detection using Berkeley Wavelet Transformation and Support Vector Machine

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1062-1065
Keyword(s):  
Support Vector Machine ◽  
Brain Tumor ◽  
Classification Accuracy ◽  
Wavelet Transformation ◽  
Tumor Detection ◽  
Classification Error ◽  
Support Vector ◽  
Dice Coefficient ◽  
Automated Method ◽  
The Brain

In this research work, a new automated system is developed for brain tumor detection by using Magnetic Resonance Imaging (MRI) on the basis of machine learning techniques. The major concerns in the brain tumor detection are time consuming, and the classification accuracy dependsonly on clinician’s experience. To address these issues, a new supervised system is developed for brain tumor detection. In this research study, a new segmentation approach was used for improving the brain tumor detection performance and to diminish the complexity of the system. Initially, Anisotropic Diffusion Filter (ADF) was used as an image pre-processing technique for removing noise from the collected brain image. Then, Berkeley Wavelet Transformation (BWT) was utilized for converting the spatial form of pre-processed MRI image into temporal domain frequency. Besides, Support Vector Machine (SVM) was usedas a classification technique to classify the normal and abnormal regions. SVM classifier effectively diminishes the size of resulting dual issue by developing a relaxed classification error bound. In addition, the undertaken classification approach quickly speed up the training process by maintaining a competitive classification accuracy. From the experimental analysis, the proposed system improved dice coefficient >0.9 compared to the existing systems. The experimental investigation validated and evaluated that the proposed system showed good performance related to the existing systems in light of dice coefficient and accuracy.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

scholarly journals Exploring Statistical Parameters of Machine Learning Techniques for Detection and Classification of Brain Tumor

2019 ◽  
Vol 8 (10) ◽  
pp. 4118-4124
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Computerized System ◽  
The Brain

A computerized system can improve the disease identifying abilities of doctor and also reduce the time needed for the identification and decision-making in healthcare. Gliomas are the brain tumors that can be labeled as Benign (non- cancerous) or Malignant (cancerous) tumor. Hence, the different stages of the tumor are extremely important for identification of appropriate medication. In this paper, a system has been proposed to detect brain tumor of different stages by MR images. The proposed system uses Fuzzy C-Mean (FCM) as a clustering technique for better outcome. The main focus in this paper is to refine the required features in two steps with the help of Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) using three machine learning techniques i.e. Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The final outcome of our experiment indicated that the proposed computerized system identifies the brain tumor using RF, ANN and SVM with 100%, 91.6% and 95.8%, accuracy respectively. We have also calculated Sensitivity, Specificity, Matthews’s Correlation Coefficient and AUC-ROC curve. Random forest shows the highest accuracy as compared to Support Vector Machine and Artificial Neural Networks.

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