Three‐class brain tumor classification from magnetic resonance images using separable convolution based neural network

2021 ◽  
Author(s):  
Bala Venkateswarlu Isunuri ◽  
Jagadeesh Kakarla
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Tumor Classification

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 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 using Deep Learning Algorithms

2021 ◽  
Vol 9 (12) ◽  
pp. 417-426
Author(s):  
Ankita Kadam
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tumor Classification ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract: A Brain tumor is one aggressive disease. An estimated more than 84,000 people will receive a primary brain tumor diagnosis in 2021 and an estimated 18,600 people will die from a malignant brain tumor (brain cancer) in 2021.[8] The best technique to detect brain tumors is by using Magnetic Resonance Imaging (MRI). More than any other cancer, brain tumors can have lasting and life-altering physical, cognitive, and psychological impacts on a patient’s life and hence faster diagnosis and best treatment plan should be devised to improve the life expectancy and well-being of these patients. Neural networks have shown colossal accuracy in image classification and segmentation problems. In this paper, we propose comparative studies of various deep learning models based on different types of Neural Networks(ANN, CNN, TL) to firstly identify brain tumors and then classify them into Benign Tumor, Malignant Tumor or Pituitary Tumor. The data set used holds 3190 images on T1-weighted contrast-enhanced images which were cleaned and augmented. The best ANN model concluded with an accuracy of 78% and the best CNN model consisting of 3 convolution layers had an accuracy of 90%. The VGG16(retrained on the dataset) model surpasses other ANN, CNN, TL models for multi-class tumor classification. This proposed network achieves significantly better performance with a validation accuracy of 94% and an F1-Score of 91. Keywords: Artificial Neural Network(ANN), Convolution Neural Network (CNN), Transfer Learning(TL), Magnetic Resonance Imaging(MRI.)

scholarly journals Brain Tumor Classification Using Neural Network Based Methods

2012 ◽  
pp. 112-117 ◽  
Author(s):  
Kailash D. Kharat ◽  
Pradyumna P. Kulkarni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Object Recognition ◽  
Magnetic Resonance ◽  
Tumor Classification ◽  
Brain Images ◽  
Feed Forward ◽  
Artificial Neural

MRI (Magnetic resonance Imaging) brain tumor images Classification is a difficult task due to the variance and complexity of tumors. This paper presents two Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of three stages, namely, feature extraction, dimensionality reduction, and classification. In the first stage, we have obtained the features related with MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features. In the classification stage, two classifiers based on supervised machine learning have been developed. The first classifier based on feed forward artificial neural network (FF-ANN) and the second classifier based on Back-Propagation Neural Network. The classifiers have been used to classify subjects as normal or abnormal MRI brain images. Artificial Neural Networks (ANNs) have been developed for a wide range of applications such as function approximation, feature extraction, optimization, and classification. In particular, they have been developed for image enhancement, segmentation, registration, feature extraction, and object recognition and classification. Among these, object recognition and image classification is more important as it is a critical step for high-level processing such as brain tumor classification. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Hopfield, Cellular, and Pulse-Coupled neural networks have been used for image segmentation. These networks can be categorized into feed-forward (associative) and feedback (auto-associative) networks..

Sign in / Sign up

Export Citation Format

Share Document