Automated Detection of Brain Tumor in EEG Signals Using Artificial Neural Networks

2009 ◽  
Author(s):  
M. Murugesan ◽  
R. Sukanesh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Brain Tumor ◽  
Automated Detection ◽  
Eeg Signals ◽  
Artificial Neural

Comparative Study: Artificial Neural Networks Training Functions for Brain Tumor Segmentation for MRI Images

2020 ◽  
Vol 17 (4) ◽  
pp. 1831-1838
Author(s):  
T. Chithambaram ◽  
K. Perumal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Brain Tumor ◽  
Back Propagation ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Training Algorithms ◽  
Back Propagation Algorithm ◽  
Brain Tumor Segmentation ◽  
Artificial Neural

Brain tumor detection from medical images is essential to diagnose earlier and to take decision in treatment planning. Magnetic Resonance Images (MRI) is frequently preferred for detecting brain tumors by the physicians. This paper analyses various Artificial Neural Networks (ANN) training functions for brain tumor segmentation such as Levenberg-Marquardt (LM), Quasi Newton back propagation (QN), Bayesian regularization (BR), Resilient back propagation algorithm (RP) and Scaled conjugate gradient back propagation (SCG). The training algorithms were employed in different sized network for segmentation. The results were carefully analyzed and measured using Dice similarity, sensitivity, specificity and accuracy measures.

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