A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

1992 ◽  
Vol 3 (5) ◽  
pp. 672-682 ◽  
Author(s):  
L.O. Hall ◽  
A.M. Bensaid ◽  
L.P. Clarke ◽  
R.P. Velthuizen ◽  
M.S. Silbiger ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Fuzzy Clustering ◽  
Magnetic Resonance Images ◽  
Clustering Techniques ◽  
The Brain

Neural network-based segmentation of magnetic resonance images of the brain

1997 ◽  
Vol 44 (2) ◽  
pp. 194-198 ◽  
Author(s):  
J. Alirezaie ◽  
M.E. Jernigan ◽  
C. Nahmias
Keyword(s):  
Neural Network ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
The Brain

Detection of Tumor From Brain MRI Images Using Supervised and Unsupervised Methods

2021 ◽  
pp. 61-75
Author(s):  
Kannan S. ◽  
Anusuya S.
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Mri ◽  
Tumor Location ◽  
Magnetic Resonance Images ◽  
Unsupervised Clustering ◽  
Clustering Techniques ◽  
Different Tissues ◽  
Tumor Mri

Brain tumor discovery and its segmentation from the magnetic resonance images (MRI) is a difficult task that has convoluted structures that make it hard to section the tumor with MR cerebrum images, different tissues, white issue, gray issue, and cerebrospinal liquid. A mechanized grouping for brain tumor location and division helps the patients for legitimate treatment. Additionally, the method improves the analysis and decreases the indicative time. In the separation of cerebrum tumor, MRI images would focus on the size, shape, area, and surface of MRI images. In this chapter, the authors have focused various supervised and unsupervised clustering techniques for identifying brain tumor and separating it using convolutional neural network (CNN), k-means clustering, fuzzy c-means grouping, and so on.

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