Name
Title | ||
|---|---|---|
Hybridized classification approach for magnetic resonance brain images using gray wolf optimizer and support vector machine. |
Abstract | ||
|---|---|---|
Automated abnormal brain discovery is an extremely crucial task for clinical diagnosis. Over a decade ago, various techniques had been displayed to improve this technology. This paper presents a hybrid system based on a combination of Gray Wolf Optimizer (GWO) and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel to classify a given Magnetic Resonance (MR) brain image as benign or malignant. 5-fold cross validation was used to enhance generalization. We applied the hybrid system on 80 images (20 benign and 60 malignant), and found out that the classification accuracy was as high as 98.750%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-019-07876-8 | Multimedia Tools and Applications |
Keywords | Field | DocType |
MR images, Gray wolf optimizer, Support vector machine, Classification | Kernel (linear algebra),Computer vision,Radial basis function,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Clinical diagnosis,Hybrid system,Cross-validation,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
78 | 19 | 1380-7501 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heba M. Ahmed | 1 | 2 | 0.37 |
| Bayumy A. B. Youssef | 2 | 2 | 0.37 |
| Ahmed S. ElKorany | 3 | 9 | 2.86 |
| Zeinab F. Elsharkawy | 4 | 4 | 2.09 |
| Adel A. Saleeb | 5 | 2 | 0.37 |
| Fathi E. Abd El-Samie | 6 | 439 | 87.48 |