Name
Title | ||
|---|---|---|
Machine Learning Approach-Based Gamma Distribution for Brain Tumor Detection and Data Sample Imbalance Analysis. |
Abstract | ||
|---|---|---|
Recently, artificial intelligence applications in magnetic resonance imaging have been applied in several clinical studies. The analysis of brain tumors without human intervention is considered a significant area of research because the extracted brain images need to be optimized using a segmentation algorithm that is highly resilient to noise and cluster size sensitivity problems and automaticall... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2878276 | IEEE Access |
Keywords | Field | DocType |
Tumors,Image segmentation,Machine learning,Magnetic resonance imaging,Image edge detection,Machine learning algorithms | Computer science,Segmentation,Brain tumor,Mean squared error,Image segmentation,Artificial intelligence,Gamma distribution,Region of interest,Machine learning,Magnetic resonance imaging,Applications of artificial intelligence | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 2 |
PageRank | References | Authors |
0.38 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gunasekaran Manogaran | 1 | 659 | 34.70 |
| P. Mohamed Shakeel | 2 | 10 | 2.26 |
| Azza S. Hassanein | 3 | 2 | 1.40 |
| Priyan Malarvizhi Kumar | 4 | 217 | 8.32 |
| Gokulnath Chandra Babu | 5 | 67 | 5.54 |