3D Texture Features Mining for MRI Brain Tumor Identification

3D Research ◽  
2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Mohd Shafry Mohd Rahim ◽  
Tanzila Saba ◽  
Fatima Nayer ◽  
Afraz Zahra Syed
Keyword(s):  
Brain Tumor ◽  
Texture Features ◽  
Mri Brain ◽  
3D Texture ◽  
Tumor Identification ◽  
Features Mining

scholarly journals SVM-PUK Kernel Based MRI-brain Tumor Identification Using Texture and Gabor Wavelets

2019 ◽  
Vol 36 (2) ◽  
pp. 185-191 ◽  
Author(s):  
Siva Chinnam ◽  
Venkatramaphanikumar Sistla ◽  
Venkata Kolli
Keyword(s):  
Brain Tumor ◽  
Gabor Wavelets ◽  
Mri Brain ◽  
Tumor Identification

MRI brain tumor segmentation based on texture features and kernel sparse coding

2019 ◽  
Vol 47 ◽  
pp. 387-392 ◽  
Author(s):  
Jijun Tong ◽  
Yingjie Zhao ◽  
Peng Zhang ◽  
Lingyu Chen ◽  
Lurong Jiang
Keyword(s):  
Brain Tumor ◽  
Sparse Coding ◽  
Texture Features ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Brain

MRI Brain Tumor Image Analysis Using Fuzzy Rule based Approach

2019 ◽  
Vol 50 (2) ◽  
pp. 98-112 ◽  
Author(s):  
KALYAN KUMAR JENA ◽  
SASMITA MISHRA ◽  
SAROJANANDA MISHRA ◽  
SOURAV KUMAR BHOI ◽  
SOUMYA RANJAN NAYAK
Keyword(s):  
Image Analysis ◽  
Brain Tumor ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Mri Brain ◽  
Rule Based Approach

scholarly journals Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)

2021 ◽  
Vol 11 (1) ◽  
pp. 380-390
Author(s):  
Pradipta Kumar Mishra ◽  
Suresh Chandra Satapathy ◽  
Minakhi Rout
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Gray Wolf ◽  
Deep Convolutional Neural Networks ◽  
Brain Tumor Segmentation ◽  
Swarm Optimization ◽  
Brain Image ◽  
Mri Brain ◽  
Whale Optimization ◽  
The Brain

Abstract Segmentation of brain image should be done accurately as it can help to predict deadly brain tumor disease so that it can be possible to control the malicious segments of brain image if known beforehand. The accuracy of the brain tumor analysis can be enhanced through the brain tumor segmentation procedure. Earlier DCNN models do not consider the weights as of learning instances which may decrease accuracy levels of the segmentation procedure. Considering the above point, we have suggested a framework for optimizing the network parameters such as weight and bias vector of DCNN models using swarm intelligent based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The simulation results reveals that the WOA optimized DCNN segmentation model is outperformed than other three optimization based DCNN models i.e., GA-DCNN, PSO-DCNN, GWO-DCNN.

scholarly journals Bayesian MSTBurr mixture model in the construction of 3D-MRI brain tumor images

2021 ◽  
Vol 1722 ◽  
pp. 012098
Author(s):  
A A Pravitasari ◽  
N Iriawan ◽  
K Fithriasari ◽  
S W Purnami ◽  
Irhamah ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Mixture Model ◽  
3D Mri ◽  
Mri Brain
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