scholarly journals Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
R. Rajesh Sharma ◽  
P. Marikkannu
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Gravitational Search Algorithm ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Classification Model ◽  
Support Vector ◽  
Preliminary Evaluation ◽  
Distribution Method

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, andk-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

scholarly journals Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1171-1179
Author(s):  
Swaraja Kuraparthi ◽  
Madhavi K. Reddy ◽  
C.N. Sujatha ◽  
Himabindu Valiveti ◽  
Chaitanya Duggineni ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Data Augmentation ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.

scholarly journals Three-dimensional magnetic resonance reconstruction images before and after surgical therapy of spontaneous canine brain tumors

2007 ◽  
Vol 37 (4) ◽  
pp. 1174-1177
Author(s):  
Julio Carlos Canola ◽  
Fabrício Singaretti de Oliveira
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Surgical Planning ◽  
Tumor Volume ◽  
Three Dimensional ◽  
Treatment Monitoring ◽  
Magnetic Resonance Images ◽  
Intracranial Tumor ◽  
Anatomical Structures ◽  
Before And After

Specific software was used for reconstruction of spontaneous intracranial tumor volume from magnetic resonance images (MRI) in three dogs. Histopathologically confirmed meningioma, cystic meningioma, and choroid plexus tumors were evaluated before and after surgery. The software allowed the whole-volume segmentation of the skin, brain, tumor, edema, and cyst. Manipulation of the three-dimensional images (3D) allowed visualization of all anatomical structures, aided clinical understanding, surgical planning, and treatment monitoring.

scholarly journals MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2222
Author(s):  
Jaeyong Kang ◽  
Zahid Ullah ◽  
Jeonghwan Gwak
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Magnetic Resonance Imaging ◽  
Tumor Classification ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Deep Feature

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

scholarly journals A Survey of Brain Tumor Segmentation and Classification Algorithms

2021 ◽  
Vol 7 (9) ◽  
pp. 179
Author(s):  
Erena Siyoum Biratu ◽  
Friedhelm Schwenker ◽  
Yehualashet Megersa Ayano ◽  
Taye Girma Debelee
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Region Growing ◽  
Tumor Classification ◽  
Classification Model ◽  
Tumor Segmentation ◽  
Mri Scan ◽  
Brain Tumor Segmentation

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.

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