Name
Abstract | ||
|---|---|---|
Recently deep learning has been playing a major role in the field of computer vision. One of its applications is the reduction of human judgment in the diagnosis of diseases. Especially, brain tumor diagnosis requires high accuracy, where minute errors in judgment may lead to disaster. For this reason, brain tumor segmentation is an important challenge for medical purposes. Currently several methods exist for tumor segmentation but they all lack high accuracy. Here we present a solution for brain tumor segmenting by using deep learning. In this work, we studied different angles of brain MR images and applied different networks for segmentation. The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network. Experimental evaluations of the networks show that Dice score of 0.73 is achieved for a single network and 0.79 in obtained for multiple networks. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Market segmentation,Pattern recognition,Computer science,Segmentation,Brain tumor segmentation,Brain tumor,Human judgment,Sorting,Tumor segmentation,Artificial intelligence,Deep learning |
DocType | Volume | Citations |
Journal | abs/1809.07786 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahra Sobhaninia | 1 | 0 | 0.34 |
| Safiyeh Rezaei | 2 | 0 | 0.34 |
| Alireza Noroozi | 3 | 0 | 0.34 |
| Mehdi Ahmadi | 4 | 0 | 0.68 |
| Hamidreza Zarrabi | 5 | 0 | 1.69 |
| Nader Karimi | 6 | 145 | 32.75 |
| Ali Emami | 7 | 8 | 8.05 |
| Shadrokh Samavi | 8 | 233 | 38.99 |