Brain Tumor Detection and Segmentation from Magnetic Resonance Image Data Using Ensemble Learning Methods
| Autor: | Laszlo Szilagyi, Levente Kovacs, Agnes Gyorfi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Brain tumor Image segmentation Machine learning computer.software_genre medicine.disease Ensemble learning 030218 nuclear medicine & medical imaging Image (mathematics) Domain (software engineering) Random forest Data set 03 medical and health sciences 0302 clinical medicine medicine Segmentation Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) SMC |
| DOI: | 10.1109/smc.2019.8914463 |
| Popis: | The steadily growing amount of medical image data requires automatic segmentation algorithms and decision support, because at a certain time, there will not be enough human experts to establish the diagnosis for every patient. It would be a good question to establish whether this day has already arrived or not. Computerized screening and diagnosis of brain tumor is an intensively investigated domain, especially since the first Brain Tumor Segmentation Challenge (BraTS) organized seven years ago. Several ensemble learning solutions have been proposed lately to the brain tumor segmentation problem. This paper presents an evaluation framework designed to test the accuracy and efficiency of ensemble learning algorithms deployed for brain tumor segmentation using the BraTS 2016 train data set. Within this category of machine learning algorithms, random forest was found the most appropriate, both in terms of precision and runtime. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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