Name
Title | ||
|---|---|---|
Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T. |
Abstract | ||
|---|---|---|
•Machine learning-based grading of gliomas with a multi-region-of-interests approach.•An accuracy of 93.0% with a specificity of 86.7% was achieved.•The solid tumor, tumor periphery and peritumoral edema/normal regions were defined.•mp-MRI: rCBV, ADC, Cho/NAA, FA, NAA (tumor); FA, MTT (periphery); Cho/NAA (edema). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.compbiomed.2018.06.009 | Computers in Biology and Medicine |
Keywords | Field | DocType |
Machine learning,Multi-parametric magnetic resonance imaging,Gliomas | Kernel (linear algebra),Population,Diffusion MRI,Computer science,Glioma,Support vector machine,Brain tumor,Artificial intelligence,Cross-validation,Machine learning,Magnetic resonance imaging | Journal |
Volume | ISSN | Citations |
99 | 0010-4825 | 3 |
PageRank | References | Authors |
0.48 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fusun Citak Er | 1 | 3 | 0.48 |
| Zeynep Firat | 2 | 9 | 2.52 |
| Ilhami Kovanlikaya | 3 | 3 | 0.48 |
| Ture, U. | 4 | 3 | 1.15 |
| Esin Ozturk-Isik | 5 | 5 | 1.91 |