Name
Title | ||
|---|---|---|
Comparative analysis of neural model and fuzzy model for MR brain tumor image segmentation |
Abstract | ||
|---|---|---|
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these two techniques. In this paper, these two techniques are analyzed in the context of MR brain tumor image segmentation. Real time abnormal MR brain images are used in this work. A comprehensive feature vector is formed from these images. An optimization algorithm is used to select the significant features. These features are used to train the representative of neural networks namely Linear Vector Quantization (LVQ) network and the Fuzzy C-means (FCM) algorithm which belongs to the category of fuzzy systems. An extensive analysis and comparison is performed in terms of segmentation efficiency and convergence time period. Experimental results show promising results for the neural classifier over the fuzzy classifier in terms of the performance measures. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NABIC.2009.5393660 | Coimbatore |
Keywords | Field | DocType |
artificial intelligence,brain,fuzzy logic,fuzzy set theory,image segmentation,medical image processing,neural nets,optimisation,pattern classification,tumours,vector quantisation,MR brain tumor image segmentation,artificial intelligence,artificial neural networks,biological computational applications,fuzzy C-means,fuzzy classifier,fuzzy logic systems,fuzzy model,fuzzy systems,fuzzy theory,linear vector quantization,neural model,optimization,Artificial Neural Networks,Fuzzy C-means,convergence time period,segmentation efficiency | Neuro-fuzzy,Pattern recognition,Computer science,Fuzzy logic,Learning vector quantization,Fuzzy set,Image segmentation,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Artificial neural network,Machine learning | Conference |
ISSN | ISBN | Citations |
2164-7364 | 978-1-4244-5053-4 | 1 |
PageRank | References | Authors |
0.35 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D. Jude Hemanth | 1 | 1 | 0.35 |
| Kezi Selva Vijila, C. | 2 | 1 | 0.35 |
| Anitha, J. | 3 | 1 | 0.35 |