scholarly journals Hybrid enhanced ICA & KSVM based brain tumor image segmentation

2018 ◽  
Vol 14 (1) ◽  
pp. 478
Author(s):  
Thrivikram Bathini ◽  
Baswaraj Gadgay
Keyword(s):  
Magnetic Resonance ◽  
Medical Image ◽  
Automated Analysis ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Hybrid Strategy ◽  
Classifier Performance ◽  
Magnetic Resonance Imaging Mri

<span>Medical image processing is an important aspect in diagnosis and treatment strategy. The tremendous volume of medical data has accelerated the need for automated analysis of this image, more so in the case Magnetic Resonance Imaging (MRI). An improved K-means algorithm and EM algorithm have been combined in the proposed approach to produce a hybrid strategy for better clustering and segmentation using Enhanced ICA. A classifier for based on Support Vector Machine (SVM) has been formulated and employed for the classification of brain tumors in Magnetic Resonance Images (MRI). The proposed SVM classifier used a kernel in the form of Gaussian radial basis function kernel (GRB kernel) to improve the classifier performance. The performance of the classifier has been validated through expert clinical opinion and calculation of performance measures. The results amply illustrate the suitability of the proposed classifier.</span>

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 Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN

2017 ◽  
Vol 3 (1) ◽  
pp. 36-44
Author(s):  
Hema Rajini N
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Nearest Neighbor ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Network Support ◽  
First Order ◽  
Occurrence Matrix

A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demon­strated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks.

scholarly journals Alzheimer’s Disease Detection in Brain Magnetic Resonance Images Using Multiscale Fractal Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Salim Lahmiri ◽  
Mounir Boukadoum
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Multiscale Analysis ◽  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Automated System ◽  
Support Vector ◽  
Svm Classifier ◽  
Leave One Out

We present a new automated system for the detection of brain magnetic resonance images (MRI) affected by Alzheimer’s disease (AD). The MRI is analyzed by means of multiscale analysis (MSA) to obtain its fractals at six different scales. The extracted fractals are used as features to differentiate healthy brain MRI from those of AD by a support vector machine (SVM) classifier. The result of classifying 93 brain MRIs consisting of 51 images of healthy brains and 42 of brains affected by AD, using leave-one-out cross-validation method, yielded classification accuracy, 100% sensitivity, and specificity. These results and a processing time of 5.64 seconds indicate that the proposed approach may be an efficient diagnostic aid for radiologists in the screening for AD.

Sign in / Sign up

Export Citation Format

Share Document