Holistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural Network
| Autor: | Hong Zhu, Kai Xu, Changyou Chen, Jinhui Xu, Mingchen Gao, Zheshuo Li, Yufan Zhou |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
business.industry Computer science media_common.quotation_subject Population Feature extraction Brain tumor Pattern recognition medicine.disease Convolutional neural network Statistical classification Recurrent neural network medicine Contrast (vision) Segmentation Artificial intelligence education business media_common |
| Zdroj: | Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030117221 BrainLes@MICCAI (1) |
| Popis: | We present a holistic brain tumor screening and classification method for detecting and distinguishing multiple types of brain tumors on MR images. The challenges arise from the significant variations of location, shape, size, and contrast of these tumors. The proposed algorithms start with feature extraction from axial slices using dense convolutional neural networks; the obtained sequential features of multiple frames are then fed into a recurrent neural network for classification. Different from most other brain tumor classification algorithms, our framework is free from manual or automatic region of interests segmentation. The results reported on a public dataset and a population of 422 proprietary MRI scans diagnosed as normal, gliomas, meningiomas and metastatic brain tumors demonstrate the effectiveness and efficiency of our method. |
| Databáze: | OpenAIRE |
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